Systems, methods and devices for monitoring betting activities

ABSTRACT

A platform, device and process for capturing images of the surface of a gaming table and determining the quantity, identity, and arrangement of chips bet at a gaming table. Image data is captured corresponding to the one or more chips positioned in at least one betting area on a gaming surface of the respective gaming table and the data is processed to filter out the background, establish a two dimensional grid of points of interests and corresponding histograms for classifying the one or more chips through identifying a dominant classification of each row in the grid of points of interests.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 15/309,102 filed Nov. 4, 2016, which is a national phase entryof PCT/CA2016/050442 filed Apr. 15, 2016. This application claims thebenefit of and priority to U.S. Provisional Application No. 62/168,395filed May 29, 2015 and U.S. Provisional Application No. 62/298,154 filedFeb. 22, 2016. The entire contents of each of the above applications areherein incorporated by reference.

FIELD

Embodiments generally relate to the field of monitoring game activitiesat gaming tables in casinos and other gaming establishments, and inparticular, to monitoring game activities including betting activities.

INTRODUCTION

Casinos and gaming establishments may offer a variety of card games tocustomers. Card games involve various game activities, such as card playand betting, for example. A card game may be played at a gaming table byplayers, including a dealer and one or more customers. It may bedesirable for casinos or gaming establishments to monitor bettingactivities for security and management purposes.

Gaming establishments are diverse in layouts, lighting, and securitymeasures, among others. For example, betting markers, such as chips, mayhave varying designs and markings that not only distinguish between chiptypes (e.g., chip values), but also different series of chips having thesame values (e.g., to reduce the risk counterfeiting and/or to enabletracking).

SUMMARY

Embodiments described herein provide a platform, device and process formonitoring game activities at a gaming table. In particular, embodimentsdescribed herein provide a platform, device and process for capturingimages of the surface of a gaming table and determining the quantity,identity, and arrangement of chips bet at a gaming table.

Embodiments described herein can be used to capture images of thesurface of a gaming table, including gambling chips, in response tocertain events such as placing a bet. A device included in someembodiments is configured to remove background image data, for example,by distinguishing chips from the background or its surroundings based ontheir respective depth values. The device is configured to identifypoints of interest on the chip images, including across the length of aside view of each chip, and classify each point of interest usinghistogram descriptor derived from various image channels correspondingto the point of interest. The device is configured to then generate aclassification for each chip, based on the classified point of interestin a single row. The classification for each chip can be stored as aseparate value in a data structure. The device is configured to identifythe quantity, type, and arrangement of each chip based on thearrangement and identity of the values in the data structure.

Computation at each stage can be separated logically, temporally, orphysically; compartmentalized; and/or performed by separate systemcomponents to improve computation time and enable the process to becompleted in manageable steps, for example, at times or in sequencesoptimal for use of computational resources or generating a result.

The device can enable efficient transmission of data relating to bettingactivity or chip quantification and classification by generating data ateach step locally and encoding the relevant data in forms, such asvectors or other data structures, that are more easily transmitted.

Embodiments can also thereby enable efficient transmission, storage, anduse of data generated from images captured, which can allow betmonitoring and classification to be accomplished, avoiding a need fortransmission of many and large raw images to a separate server.

Embodiments described herein can capture image data across a pluralityof channels and can include processors configured to extend one or moreof the channels by transforming the image data, for example, to provideadditional data for improved processing or classification accuracy.Embodiments can aggregate data from one or more images captured overtime to reduce transient effects arising from temporary visualobstructions of an object captured. This can allow betting activities tobe monitored even in chaotic gambling situations, with movingparticipants and placement of objects within the monitored area.Horizontal gradients of the image data and/or transformed image data canbe generated to capture vertical texture in the chips and can be used,for example, to extract image data reflecting geophysical edge locationsof chips or other objects for improved image recognition.

In accordance with an aspect, there is provided a device for monitoringgame activities at a gaming table comprising an imaging componentpositioned on a gaming table or proximate thereto to capture image datacorresponding to the one or more chips positioned in at least onebetting area on a gaming surface of the respective gaming table, eachimaging component comprising one or more sensors responsive toactivation events and deactivation events to trigger capture of theimage data by the imaging component, the imaging component positioned tocapture images of a gaming surface of the respective gaming table; aprocessor configured to pre-process the captured image data to filter atleast a portion of background image data to generate a compressed set ofimage data of the one or more chips free of the background image data;the processor further configured to process the compressed set of imagedata to establish a two-dimensional grid comprising points of interestoverlaid over the compressed set of image data, and for each point ofinterest, classify the point of interest based on an analysis of acorresponding representative histogram descriptor generated based on theimage data corresponding to the corresponding point of interest; theprocessor further configured to identify a dominant classification foreach row in the grid of the points of interest, the dominantclassification recorded in a data structure to establish a vectorrepresentation of the one or more chips in the at least one bettingarea; the processor further configured to determine one or morequantities of one or more chip types of the one or more chips in the atleast one betting area by processing the vector representation based atleast on a comparison with physical geometric characteristics of the oneor more chip types; and data storage configured to maintain a datastructure storing one or more data fields representative of thedetermined one or more quantities of one or more chip types of the oneor more chips in the at least one betting area.

In accordance with an aspect, there is provided a device for monitoringgame activities at a gaming table comprising an imaging componentpositioned on a gaming table or proximate thereto to capture image datacorresponding to the one or more chips positioned in at least onebetting area on a gaming surface of the respective gaming table, eachimaging component positioned to capture images of a gaming surface ofthe respective gaming table; a processor configured to pre-process thecaptured image data to filter at least a portion of background imagedata to generate a compressed set of image data of the one or more chipsfree of the background image data; the processor further configured toprocess the compressed set of image data to establish a two-dimensionalgrid comprising points of interest overlaid over the compressed set ofimage data, and for each point of interest, classify the point ofinterest based on an analysis of a corresponding representativehistogram generated based on the image data corresponding to thecorresponding point of interest; the processor further configured toidentify a dominant classification for each row in the grid of thepoints of interest, the dominant classification recorded in a datastructure to establish a vector representation of the one or more chipsin the at least one betting area; the processor further configured todetermine one or more quantities of one or more chip types of the one ormore chips in the at least one betting area by processing the vectorrepresentation based at least on a comparison with physical geometriccharacteristics of the one or more chip types; and data storageconfigured to maintain a data structure storing one or more data fieldsrepresentative of the determined one or more quantities of one or morechip types of the one or more chips in the at least one betting area.

In accordance with an aspect, there is provided the device furthercomprising a communication link configured for transmitting the datastructure or a subset of the data structure to generate betting data forthe gaming table, the betting data including betting amounts for the atleast one betting area.

In accordance with an aspect, there is provided a device wherein thecaptured image data is captured across a plurality of channels includingat least a red channel, a green channel, a blue channel, a depthinformation channel, and an infrared channel; and wherein eachrepresentative histogram is an aggregated histogram generated fromcombining histograms generated for each channel of the plurality of thechannels.

In accordance with an aspect, there is provided a device wherein theprocessor is configured to extend the plurality of channels bytransforming the captured image data from RGB to a different colourspace where intensity is decoupled from color information, thetransformations yielding additional channels in the plurality ofchannels.

In accordance with an aspect, there is provided a device wherein theprocessor is configured to extend the plurality of channels bytransforming the captured image data from RGB to a different colourspace where luminance is decoupled from chrominance, the transformationsyielding additional channels in the plurality of channels.

In accordance with an aspect, there is provided a device whereinhorizontal gradients are calculated using a 3×3 Sobel operator in orderto capture the vertical texture in the chips.

In accordance with an aspect, there is provided a device whereinhorizontal gradients are calculated using a 3×3 Sobel operator in orderto capture the vertical texture in the chips based on the R, G, and Bchannels.

In accordance with an aspect, there is provided a device wherein the 3×3Sobel operator is used on the R, G, and B channels.

In accordance with an aspect, there is provided a device wherein thecaptured image data is represented by an aggregated frame correspondingto average image data of image data captured across a duration of timeto reduce transient effects arising from temporary visual obstructionsof the imaging component.

In accordance with an aspect, there is provided a device wherein theprocessor is further configured to pre-process the captured image datato apply at least one of rotation and scale invariance.

In accordance with an aspect, there is provided a device wherein thedominant classifications are determined by utilizing a trained randomforest classifier; and wherein the trained random forest classifier isoptimized during training in relation to at least one of (i) criterionfor a decision split, (ii) a number of features for consideration fordetermining the criterion for the decision split, (iii) a number oftrees in the forest, (iv) a minimum number of samples required to splitan internal node, (v) a maximum depth of a tree, and (vi) use ofbootstrap samples.

In accordance with an aspect, there is provided a device wherein thedominant classifications are determined by utilizing a trained randomforest classifier and wherein the trained random forest classifierincludes a plurality of classification trees and each representativehistogram is classified by a classification identified by a majority ofthe plurality of the classification trees.

In accordance with an aspect, there is provided a device wherein thedominant classification for each row in the grid of the points ofinterest is determined by a classification type representing a largestproportion of the points of interest in the row in the grid.

In accordance with an aspect, there is provided a device wherein thedetermination of the one or more quantities of the one or more chiptypes of the one or more chips in the at least one betting area includesidentifying one or more chip volumes within the vector representation bygrouping similar classifications in the vector representation, each chipvolume representing a stack of chips having similar classifications;determining a centroid for each chip volume of the one or more chipvolumes; identifying a height for each chip volume; and estimating anumber of chips in each chip volume by comparing the centroid and theheight of each chip volume with the physical geometric characteristicsof the one or more chip types, the estimated number of chips in eachchip volume utilized to determine the one or more quantities of the oneor more chip types.

In various further aspects, the disclosure provides correspondingmethods, systems and devices, and logic structures such asmachine-executable coded instruction sets for implementing such systems,devices, and methods.

In an aspect, there is provided a system for monitoring game activitiesat a plurality of gaming tables comprising: a plurality of clienthardware devices for the plurality of gaming tables, each clienthardware device comprising an imaging component positioned on arespective gaming table or proximate thereto to capture image datacorresponding to the one or more chips positioned in a betting area on agaming surface of the respective gaming table and, in response,pre-processing the captured image data to generate a compressed set ofimage data free of background image data, each client hardware devicecomprising one or more sensors responsive to activation events anddeactivation events to trigger capture of the image data by the imagingcomponent; a game monitoring server for collecting, processing andaggregating the compressed image data from the client hardware devicesto generate aggregated betting data for the plurality of gaming tables;and a front end interface device for displaying the aggregated bettingdata from the game monitoring server for provision to or display on enduser systems, the front end interface device for receiving controlcommands from the end user systems for controlling the provision ordisplay of the aggregated betting data.

In another aspect, the imaging component is positioned to capture theimage data at an offset angle relative to a plane of the gaming surfaceof the respective gaming table; and wherein the offset angle permits theimaging component to capture the image data from sidewalls of the one ormore chips.

In another aspect, the offset angle is an angle selected from the groupof angles consisting of about −5 degrees, about −4 degrees, about −3degrees, about −2 degrees, about −1 degrees, about 0 degrees, about 1degrees, about 2 degrees, about 3 degrees, about 4 degrees, and about 5degrees; and the altitude is an altitude selected from the group ofaltitudes consisting of about 0.2 cm, about 0.3 cm, about 0.4 cm, about0.5 cm, about 0.6 cm, about 0.7 cm, about 0.8 cm, about 0.9 cm, andabout 1.0 cm.

In another aspect, the system further comprises an illumination stripadapted to provide a reference illumination on the one or more chips,the illumination strip positioned at a second substantially horizontalangle to provide illumination on the sidewalls of the one or more chips;the second substantially horizontal angle selected such that thepresence of shadows on the one or more chips is reduced.

In another aspect, the illumination strip is controllable by the clienthardware devices and configured to provide the reference illumination inaccordance with control signals received from the client hardwaredevices; the control signals, when processed by the illumination strip,cause the illumination strip to change an intensity of the referenceillumination based at least on ambient lighting conditions, the controlsignals adapted to implement a feedback loop wherein the referenceillumination on the one or more chips is substantially constant despitechanges to the ambient lighting conditions.

In another aspect, the one or more sensors are adapted to determine oneor more depth values corresponding to one or more distances from areference point to the one or more chips, each of the depth valuescorresponding to the distance to a corresponding chip.

In another aspect, the one or more sensors determine the one or moredepth values by using at least one of Doppler radar measurements,parallax measurements, infrared thermography, shadow measurements, lightintensity measurements, relative size measurements, and illuminationgrid measurements.

In another aspect, the one or more sensors include at least two sensorsconfigured to determine the one or more depth values by measuring stereoparallax.

In another aspect, at least one of the client hardware devices and thegame monitoring server are configured to determine a presence of one ormore obstructing objects that are partially or fully obstructing the oneor more chips from being sensed by the one or more sensors, the presenceof the one or more obstructing objects being determined by continuouslymonitoring the one or more depth values to track when the one or moredepth values abruptly changes responsive to the obstruction.

In another aspect, at least one of the client hardware devices and thegame monitoring server are configured to, responsive to positivelydetermining the presence of the one or more obstructing objects that arepartially or fully obstructing the one or more chips from being sensedby the one or more sensors, aggregate a plurality of captured imagesover a duration of time and to compare differences between each of theplurality of captured images to estimate the presence of the one or morechips despite the presence of the one or more obstructing objects thatare partially or fully obstructing the one or more chips from beingsensed by the one or more sensors.

In another aspect, the compressed set of image data free of backgroundimage data is obtained by using an estimated chip stack height incombination with the more one or more depth values to determine a chipstack bounding box that is used for differentiating between thebackground image data and chip image data during the pre-processing.

In another aspect, the game monitoring server is configured to processthe compressed set of image data free to individually identify one ormore specific chips of the one or more chips within the chip stackbounding box represented by the compressed set of image data, eachspecific chip being identified through a chip bounding box establishedaround the pixels representing the specific chip.

In another aspect, the game monitoring server is configured to identifyone or more chip values associated with each of the one or more chipswithin the chip stack bounding box by estimating a chip value based onmachine-vision interpretable features present on the one or more chips.

In another aspect, the game monitoring server is configured to identifythe one or more chip values by generating one or more histograms, eachof histogram corresponding with image data in the corresponding chipbounding box, by processing the one or more histograms to obtain one ormore waveforms, each waveform corresponding to a histogram; and the gamemonitoring server is configured to perform feature recognition on eachwaveform to compare each waveform against a library of pre-definedreference waveforms to estimate the one or more chip values throughidentifying the pre-defined reference waveform that has the greatestsimilarity to the waveform.

In another aspect, the processing of the one or more histograms toobtain the one or more waveforms includes at least performing a Fouriertransformation on the one or more histograms to obtain one or more plotsdecomposing each histogram into a series of periodic waveforms which inaggregation form the histogram.

In another aspect, the machine-vision interpretable features present onthe one or more chips include at least one of size, shape, pattern, andcolor.

In another aspect, the machine-vision interpretable features present onthe one or more chips include at least one of size, shape, pattern, andcolor and the one or more waveforms differ from one another at least dueto the presence of the machine-vision interpretable features.

In another aspect, the activation events and deactivation eventscomprising placement and removal of the one or more chips within a fieldof view of the imaging component.

In another aspect, the activation events and deactivation events aretriggered by a signal received from an external transmitter, theexternal transmitter being a transmitting device coupled to a dealershoe that transmits a signal whenever the dealer shoe is operated.

In another aspect, the system further includes an interface engineadapted to provision an interface providing real or near-real-timebetting data to a dealer, the real or near-real-time betting data basedon the betting data extracted by the game monitoring server from thecaptured image data, the betting data including one or more estimatedvalues for each stack of chips in one or more betting areas of thegaming surface.

In another aspect, there is provided a system for monitoring gameactivities comprising: a game monitoring server for collecting,processing and aggregating betting data from a plurality of clienthardware devices to generate aggregated betting data for a plurality ofgaming tables, each client hardware device having at least one imagingcomponent positioned substantially parallel to a gaming surface of arespective gaming table and configured to capture image datacorresponding to one or more chips positioned on the gaming surface inresponse to activation events, the betting data derived from the imagedata; and a front end interface device for displaying the aggregatedbetting data from the game monitoring server for provision to or displayon end user systems, the front end interface device for receivingcontrol commands from the end user systems for controlling the provisionor display of the aggregated betting data.

In another aspect, the imaging component is positioned to capture theimage data at an offset angle relative to a plane of the gaming surfaceof the respective gaming table; and wherein the offset angle permits theimaging component to capture the image data from sidewalls of the one ormore chips.

In another aspect, there is provided a device for monitoring gameactivities at a plurality of gaming tables comprising: an imagingcomponent positioned on a respective gaming table or proximate theretoto capture image data corresponding to the one or more chips positionedin a betting area on a gaming surface of the respective gaming tableand, in response, pre-processing the captured image data to generate acompressed set of image data free of background image data, each clienthardware device comprising one or more sensors responsive to activationevents and deactivation events to trigger capture of the image data bythe imaging component, the imaging component positioned substantiallyparallel to a gaming surface of the respective gaming table; a processorconfigured to pre-process the captured image data to generate acompressed set of image data free of background image data responsive toactivation events and deactivation events to trigger collection ofbetting events; and a communication link configured for transmitting thecompressed set of image data to a game monitoring server configured togenerate aggregated betting data for the plurality of gaming tables, thegenerated aggregated betting data being provided to a front endinterface device configured for displaying the aggregated betting datafrom the game monitoring server for provision to or display on end usersystems, the front end interface device configured for receiving controlcommands from the end user systems for controlling the provision ordisplay of the aggregated betting data.

In another aspect, there is provided a method for monitoring bettingactivities comprising: detecting, by an imaging component, that one ormore chips have been placed in one or more defined bet areas on a gamingsurface, each chip of the one or more chips having one or more visualidentifiers representative of a face value associated with the chip, theone or more chips arranged in one or more stacks of chips; capturing, bythe imaging component, image data corresponding to the one or more chipspositioned on the gaming surface, the capturing triggered by thedetection that the one or more chips have been placed in the one or moredefined bet areas; transforming, by an image processing engine, theimage data to generate a subset of the image data relating to the one ormore stacks of chips, the subset of image data isolating images of theone or more stacks from the image data; recognizing, by an imagerecognizer engine, the one or more chips composing the one or morestacks, the recognizer engine generating and associating metadatarepresentative of (i) a timestamp corresponding to when the image datawas obtained, (ii) one or more estimated position values associated withthe one or more chips, and (iii) one or more face values associated withthe one or more chips based on the presence of the one or more visualidentifiers; segmenting, by the image recognizer engine, the subset ofimage data and with the metadata representative of the one or moreestimated position values with the one or more chips to generate one ormore processed image segments, each processed image segmentcorresponding to a chip of the one or more chips and including metadataindicative of an estimated face value and position; and determining, bya game monitoring engine, one or more bet data values, each bet datavalue corresponding to a bet area of the one or more defined bet areas,and determined using at least the number of chips visible in each of theone or more bet areas extracted from the processed image segments andthe metadata indicative of the face value of the one or more chips.

In another aspect, the method further comprises transmitting, the one ormore bet data values corresponding to the one or more defined bet areas,to a gaming data repository, the game data repository configured forassociating the one or more bet data values to one or more bets made byone or more players as the one or more players interact with a gamebeing played on the gaming surface; and generating, on a display of acomputing device by n interface component, an electronic dashboardillustrative of at least one of current and historical bets made by theone or more players.

In this respect, before explaining at least one embodiment in detail, itis to be understood that the embodiments are not limited in applicationto the details of construction and to the arrangements of the componentsset forth in the following description or illustrated in the drawings.Also, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting.

Many further features and combinations thereof concerning embodimentsdescribed herein will appear to those skilled in the art following areading of the instant disclosure.

DESCRIPTION OF THE FIGURES

In the figures, which depict example embodiments:

FIGS. 1A and 1B illustrate a block diagrams of a system for monitoringbetting activities at gaming tables according to some embodiments.

FIG. 2 illustrates a block diagram of another system for monitoring gameactivities at gaming tables according to some embodiments.

FIG. 3 illustrates a block diagram of another system for monitoring gameactivities at gaming tables according to some embodiments.

FIGS. 4A-4C illustrates a schematic diagram of bet regions monitored bya bet recognition device according to some embodiments.

FIGS. 5 to 7 illustrate example images taken from a bet recognitiondevice mounted on a gaming table according to some embodiments.

FIGS. 8 and 9 illustrate example images of a bet recognition devicemounted on a gaming table according to some embodiments.

FIGS. 10 and 11 illustrate example images of a bet recognition deviceaccording to some embodiments.

FIG. 12 illustrate a schematic diagram of another example betrecognition device according to some embodiments.

FIGS. 13A, 13B and 14 illustrate example images from a bet recognitiondevice and processed images after transformation by server according tosome embodiments.

FIG. 15 illustrates a schematic diagram of a sensor array device for betrecognition device according to some embodiments.

FIG. 16 illustrates a schematic graph of the amplitude of the receivedsignal over time according to some embodiments.

FIG. 17 illustrates a schematic of a game monitoring server according tosome embodiments.

FIG. 18 illustrates a schematic of a bet recognition device according tosome embodiments.

FIGS. 19-23, 24A-24D, 25A-25E, 26 to 39 illustrate schematic diagrams ofbet recognition devices with camera layouts according to someembodiments.

FIGS. 40 to 43 illustrate schematic diagrams of shoe devices accordingto some embodiments.

FIGS. 44, 45, 46A-46C illustrate schematic diagrams of bet recognitiondevices with shoe devices according to some embodiments.

FIGS. 47 to 50 illustrate schematic diagrams of chip stacks according tosome embodiments.

FIGS. 51 and 52 illustrate schematic diagrams of bet recognition deviceswith camera layouts according to some embodiments.

FIGS. 53-56 are sample workflows, according to some embodiments.

FIG. 57 is a workflow diagram of an example process for a betrecognition system according to some embodiments.

FIG. 58 is a view of an example bet recognition system according to someembodiments.

FIG. 59 is a diagram of an example playing surface with betting areasand fields of view of imaging components according to some embodiments.

FIG. 60 is a diagram of an example playing surface with betting areasand fields of view of imaging components according to some embodiments.

FIG. 61 is a diagram of an example set of image data according to someembodiments.

FIG. 62 is a diagram of an example representation of image data showingdepth on a green channel according to some embodiments.

FIG. 63 is a diagram of example sets of image frames with and withoutobstruction according to some embodiments.

FIG. 64 is a diagram of example image frames according to someembodiments.

FIG. 65 is a diagram of example image frames according to someembodiments.

FIG. 66 is an example representation of image data of a stack of chipspositioned on a two-dimensional grid according to some embodiments.

FIG. 67 is an example representation of image data of a stack of chipsaccording to some embodiments.

FIG. 68 is an example of two sets of image data each containing arepresentation of image data collected over various channels accordingto some embodiments.

FIG. 69 is an example image captured by bet recognition system accordingto some embodiments.

FIG. 70 is an example image set of image data, depicting a Y colourspace, Cb colour space, and Cr colour space according to someembodiments.

FIG. 71 are example representations of a betting object (a stack ofchips) and a two-dimension representation of its horizontal gradient.

FIG. 72 is an example representation of localization of two bettingobjects on a betting surface by bet recognition system according to someembodiments.

FIG. 73 is an example image with an overlay depicting image data usedfor image recognition bet recognition system according to someembodiments.

FIG. 74 is an example image with an overlay of selected points ofinterest according to some embodiments.

FIG. 75 is an example diagram depicting pixel representations of pointsof interest selected along a betting object according to someembodiments.

FIG. 76 is an example diagram depicting each histogram generated forpoints of interest according to some embodiments.

FIG. 77 is an image of an example depiction of image data used forclassification of regions of interest according to some embodiments.

FIG. 78 is a diagram of an example classification for points of interestbased on the histogram data for each point of interest.

FIG. 79 is a diagram of an example process for training a classifier forpredicting chip type.

FIG. 80 is a diagram of example decision trees used by random forestclassifiers for classifying a point of interest according to someembodiments.

FIG. 81 is a diagram of an example mapping between classification namesand colour representations according to some embodiments.

FIG. 82 is a diagram of an example depiction for a chip quantificationprocess by bet recognition system according to some embodiments.

FIG. 83 is a diagram of an example depiction for a chip quantificationprocess by bet recognition system according to some embodiments.

FIG. 84 is a photograph of several illuminating components (e.g., LEDs)in use that are full colour and customizable for one or more tables,according to some embodiments.

FIG. 85 depicts several illuminating components (e.g., LEDs) in use thathave spreads of different color respectively, according to someembodiments.

FIG. 86 depicts illuminating component with a custom color, according tosome embodiments.

FIG. 87 is a photograph of illuminating component, depictingilluminating component from a perspective view, according to someembodiments.

FIG. 88 is an overhead photograph of physical hand count indicator,according to some embodiments.

FIG. 89 is a rear perspective view of a bet recognition device that ismounted on a table surface, according to some embodiments.

FIG. 90 is a perspective view of a bet recognition device, according tosome embodiments.

FIG. 91 is an overhead photograph illustrating two controllers, eachbeing used to drive the bet recognition devices, according to someembodiments.

FIG. 92 is a photograph illustrating potential connections between theone or more processors according to some embodiments.

FIG. 93 is a photograph of a table without felt, illustrating areas uponwhich a physical retrofit can be applied to connect bet recognitiondevice.

FIG. 94 is a photograph of under table connectors, according to someembodiments.

FIG. 95 is a rendering showing two interfaces that may be connected tobet recognition devices, according to some embodiments.

FIG. 96 illustrates an example playing surface with betting areas.

FIG. 97 is a representation of image data of a stack of chips beforerotation invariance, according to some embodiments.

FIG. 98 is a representation of image data of a stack of chips beforescale invariance, according to some embodiments.

FIG. 99 is a block diagram of a system for monitoring betting activitiesat gaming tables according to some embodiments.

DETAILED DESCRIPTION

Accurate monitoring of betting activities can be used to identify anddeter people who unfairly gain an advantage during participation in thebetting activities, for example, those who place bets when an outcome ofa game is more readily discernable. This behaviour can disadvantageother game participants who place bets at the proper time and whosewinnings can depend on other participants not winning. Participants whoengage in unfair behaviour can disproportion the odds of winning intheir favour over other game participants. In addition, casinos andother gambling establishments can lose money to these participants.Participants are more inclined to engage in unfair betting behaviour andmore likely to do so unnoticed when their activities are not monitored.There is a need for accurate monitoring of betting activities that isnot limited by the capacity of human detection.

Further, casinos and other gambling establishments can benefit from dataon game participants and their betting patterns. This information can beused to target participants who engage in riskier betting patterns so asencourage their continued participation in betting activities, forexample. Betting patterns or statistics related to betting activitiescan be used in a nuanced way to offer other incentive for certainbehaviour. It is difficult for this data to be identified, understood,and capitalized on in popular gambling establishments where there aremany game participants who engage with different games and havedifferent gambling styles.

There is a need for accurate monitoring and generation of data that canuncover useful trends in betting activities.

Transmission of many and/or large images to a remote computationalprocessing system or computer can be slow and therefore unfeasible foruse in monitoring betting activities, where decisions may need to bemade in near real-time.

Embodiments described herein provide a platform, device and process formonitoring game activities at a gaming table. In particular, embodimentsdescribed herein can provide a platform, device and process forcapturing images of the surface of a gaming table and determining thequantity, identity, and arrangement of chips bet at a gaming table.

Embodiments described herein can be used to capture images of thesurface of a gaming table, including gambling chips, in response tocertain events such as placing a bet. A device included in someembodiments is configured to remove background image data, for example,by distinguishing chips from the background based on their respectivedepth values. The device is configured to identify points of interest onthe chip images, including across the length of a side view of eachchip, and classify each point of interest using histogram data of imagechannels corresponding to the point of interest. The device isconfigured to then decide on a classification for each chip, based onthe classifications for each point of interest in a single row. Thedecided classification for each chip can be stored as a separate valuein a data structure. The device is configured to identify the quantity,type, and arrangement of each chip based on the arrangement and identityof the values in the data structure. This may involve determining groupsof similar classifications and determining the number of chipscorresponding to each group.

Computation at each stage can be separated logically, temporally, orphysically; compartmentalized; and/or performed by separate systemcomponents to improve computation time and enable the process to becompleted in manageable steps, for example, at times or in sequencesoptimal for use of computational resources or generating a result.

The device can enable efficient transmission of data relating to bettingactivity or chip quantification and classification by generating data ateach step locally and encoding the relevant data in forms, such asvectors or other data structures, that are more easily transmitted.

Embodiments can also thereby enable efficient transmission, storage, anduse of data generated from images captured, which can allow betmonitoring and classification to be accomplished, avoiding a need fortransmission of the many and large raw images to a separate server.

Embodiments described herein can capture image data across a pluralityof channels and can include processors configured to extend one or moreof the channels by transforming the image data, for example, to provideadditional data for improved processing or classification accuracy.Embodiments can aggregate data from one or more images captured overtime to reduce transient effects arising from temporary visualobstructions of an object captured. This can allow betting activities tobe monitored even in chaotic gambling situations, with movingparticipants and placement of objects within the monitored area.Horizontal gradients of the image data and/or transformed image data canbe generated to capture vertical texture in the chips and can be used,for example, to extract image data reflecting geophysical edge locationsof chips or other objects for improved image recognition.

Embodiments of methods, systems, and apparatus are described throughreference to the drawings.

Embodiments described herein relate to systems, methods and devices formonitoring game activities at gaming tables in casinos and other gamingestablishments. For example, embodiments described herein relate tosystems, methods and devices for monitoring card game activities atgaming tables. Each player, including the dealer and customer(s), may bedealt a card hand. Embodiments described herein may include devices andsystems particularly configured to monitor game activities that includebetting activities at gaming tables to determine bet data including anumber of chips in a betting area of the gaming table and a total valueof chips in the betting area.

The player bet data may be used by casino operators and third partiesfor data analytics, security, customer promotions, casino management,and so on. Games are not necessarily limited to card games, and mayinclude dice games, event betting, other table games, among others.

In accordance with an aspect of embodiments described herein, monitoringdevices may be used to retrofit gaming tables. The monitoring devicesmay be integrated with the gaming tables to provide a smooth workingarea in a manner that does not catch on cards or chips. The monitoringdevice may not require changing of a gaming table top as it may beintegrate within existing table top structure. An example of amonitoring device is a bet recognition device, as described herein.

Tracking bet activities that are on-going at a gaming facility is anon-trivial task that has myriad financial consequences. Accurate bettracking is important as it may be used to more closely monitor therevenues and outflows of the gaming facility, identify patterns (e.g.,theft, collusion), and provide an enhanced gaming experience. Forexample, tracked bet information, in the form of betting records, may beused to determine compensation levels for loyal players (e.g., theaccurate provisioning of “comps” in relation to overall casino returns),rebates, etc., or track dealer and/or game performance.

Bets are often performed in conjunction with games (e.g., baccarat,poker, craps, roulette) or events (e.g., horse racing, professionalsports, political outcomes), and traditionally, some bets are placedwith the aid of specially configured markers (e.g., chips). These betmarkers may have various markings on them, and are often distinguishedfrom one another so that it is easy to track the value of each of themarkers (e.g., denominations, characteristics). Some of the markers aredesigned with a particular facility in mind, and accordingly, may varyfrom facility to facility. For example, facilities may include casinos,gaming halls, among others.

Betting markers, such as chips, may have varying designs and markingsthat not only distinguish between chip types (e.g., chip values), butalso different series of chips having the same values (e.g., to reducethe risk counterfeiting and/or to enable tracking). For example, suchvariations may be purposefully and periodically introduced such thatcounterfeiters may have a harder time successfully copying chip designs.

Accordingly, a flexible implementation may provide a diverse range ofconditions and chips can be used with the system. For example, in someembodiments, a system is provided that is configured for interoperationwith a diverse range of chip types, and also to flexibly adapt in viewof modifications to chip designs and markings. In such embodiments, thesystem is not “hard coded” to associate specific designs and markingswith chip values, but rather, applies machine-learning to dynamicallyassociate and create linkages as new chip types are introduced into thesystem. Interoperability may be further beneficial where a single systemcan be provisioned to different gaming facilities having different needsand environments, and the system may, in some embodiments, adaptflexibly in response to such differences (e.g., by modifyingcharacteristics of a reference illumination on the chips, adaptingdefined feature recognition linkages, adapting imaging characteristics,image data processing steps, etc.).

The bet markers, such as chips, are often provided in physical form andplaced individually or in “stacks” that are provided in specific bettingareas on tables so that a dealer can see that a player has made a bet ona particular outcome and/or during a betting round. A game or event mayinclude multiple betting rounds, where a player is able to make aparticular bet in conjunction with a phase and/or a round in the game orevent. The betting may result in a win, loss, push, or other outcome,and the player may be paid chips equivalent to an amount of winnings.

The ability to track bets in real or near-real time may be of commercialand financial importance to a gaming facility. Inaccurate tracking ofbets may lead to increased management overhead and/or an inability toaccurate track betting, which may, for example, lead to missedopportunities to enhance player experience, or missed malicious behaviortrends. For example, analyzing betting patterns may indicate that someplayers are “gaming the system” by placing suspicious bets (e.g., due tocard counting, hole carding), or may indicate particularly profitablebets for the gaming facility (e.g., Blackjack insurance bets). The bettracking information may be utilized in conjunction with other types ofbackend systems, such as a hand counting system, a security managementsystem, a player compensation system (e.g., for calculating whencomplimentary items/bonuses are provided), etc. Bet recognition may alsobe used in gaming training systems, where players can be informed thattheir betting was not efficient or suboptimal based on computer-basedsimulation and calculation of odds (e.g., for Texas Hold-em poker,efficient betting may be determined based on mathematical odds and tablepositioning, especially for structured betting games and/or pot-limitand limit games, and may also be influenced by the presence of rulemodifications).

In some embodiments, bet tracking information is collected usingmachine-vision capable sensors that may be present on a gaming table orsurface, or other type of gaming machine. These machine-vision capablesensors monitor betting areas to determine the types of chips placed inthem, and estimate the value of bets, tracking betting as bettingprogresses from round to round and from game to game. As many gamingfacilities have invested significantly into their existing chips,tables, technologies and/or layouts, some embodiments described hereinare designed for flexibility and interoperation with a variety ofexisting technologies and architectures. Machine vision is not limitedto imaging in the visual spectrum, but may also include, in variousembodiments, imaging in other frequency spectra, RADAR, SONAR, etc.Machine vision may include image processing techniques, such asfiltering, registration, stitching, thresholding, pixel counting,segmentation, edge detection, optical character recognition, amongothers.

Accordingly, a bet tracking system may benefit from being able to beretrofit into existing tables and/or layouts, and interface with othertable and/or gaming facility management systems (e.g., to communicateinformation regarding betting activities). Machine-learning techniques(e.g., random forests) may be utilized and refined such that visualfeatures representative of different chip values are readily identified,despite variations between different facilities, lighting conditions andchip types. For example, such a system may not necessarily need to havehard-coded reference libraries of what chips should look like for eachvalue, and instead, may be flexibly provisioned during the calibrationprocess to build a reference library using real-world images of chips totrain a base set of features. Accordingly, in some embodiments, thesystem may be utilized without a priori knowledge of the markers presenton the various betting markers, such as chips. This may be useful wherea system may need to account for introduced variations in chip design,which, for security reasons, are not distributed ahead of introduction.

A potential challenge with tracking bets is that there are a diversityof betting markers, objects on a gaming surface, lighting conditionsthat may lead to complexities in relation to accurately determining whatbet markers are present, and further, what value should be attributed toa bet. Bets may be placed off-center by players, chips may not beuniformly stacked, chips may be obscuring one another, players mayobscure bets using their hands, players may be deliberately modifyingtheir bets (e.g., surreptitiously adding to a bet after cards have beendealt to obtain a higher payout), etc. Bet recognition can be conductedwith minimal disruption to the operations of the gaming facility orplayer experience.

There may also be limitations on the amount of available computingresources, and given that many gaming tables operate with a high volumeof games per hour, there is limited time available for processing(especially where bet data is being tracked in real or near-real time).Gaming facilities may have computational resources available atdifferent locations, and these locations may need to communicate withone another over limited bandwidth connections. For example, there maybe some computing components provided at or near a gaming table suchthat pre-processing may be conducted on sensory data, so that acompressed and/or extracted set of data may be passed to a backend formore computationally intensive analysis. In some embodiments, thebackend may revert computed information back to the computing componentsprovided at or near a gaming table so that a dealer or a pit-boss, orother gaming employee may use an interface to monitor betting activities(e.g., to determine “comp” amounts, track suspicious betting patterns,identify miscalculated payouts).

Bet recognitions systems may utilize sensors positioned at a variety ofdifferent locations to obtain information. For example, systems mayutilize overhead cameras, such as existing security cameras. A challengewith overhead camera systems is that the presence of shadows, skewedimage angles, obstructions, have rendered some embodiments particularlycomplicated from a computational perspective, as issues relating to dataquality and the amount of visible information may lead to unacceptablylow accuracy and/or confidence in computationally estimated bet counts.

Embodiments described herein provide unconventional solutions to manyproblems, including accurately monitoring and identifying bet volumes ata speed feasible for use in live games to allow for intervention duringthe game or otherwise soon after a betting amount is changed. Forexample, the use of single key frames to represent relevant bet volumesinstead of a large number of frames helps overcome technologicalproblems of slow image processing times to prepare images for use aswell as long times for transmission of images to servers for processing.Embodiments herein described also provide an ability to be retrofittedat a betting surface or gaming table, for example, at a chip tray, so asto further overcome problems of requiring transmission to a remoteserver for processing or use.

Embodiments described herein provide an improved process for usingimaging components and processing units to register images and isolateor localize regions of interest. For example, embodiments describedherein provide for strategically placed imaging components withoverlapping fields of view that each capture channels of images (RGB,IR), as well as processing components that generate depth images; extendthose channels, for example, to generate more data for increasedaccuracy in image classification and determining bet volume; andregister the images captured or generated to generate an averaged keyframe image representing relevant data across the channels, for example,by averaging only those registered frames that do not have transientobstructions. The imaging components in some embodiments use one or moresensors to provide a solution to problems, including improving accuracyand consistency in image registration and accurately determining thedistance away an object in an image is, for example, to removebackground data. This removal can allow for an ability to localize betvolumes at a betting surface, as well as improve processing andtransmission speeds, for example, by reducing the amount of data forprocessing or transmission, reducing bandwidth, or reducing potentialfor data packets to be dropped.

FIG. 1A illustrates a block diagram of a system 100A for monitoringbetting activities at gaming tables according to some embodiments. Thesystem may be configured such that sensors and/or imaging components areutilized to track betting activities, generating sensory data that issent to a backend for processing. The betting activities may be providedin the form of chips being placed in betting areas, and the sensorsand/or imaging components may include machine-vision sensors adapted forcapturing images of the betting areas.

As depicted, the system includes bet recognition devices 30 (1 to N)integrated with gaming tables (1 to N). The bet recognition devices 30may include various sensors and imaging components, among other physicalhardware devices.

Each bet recognition device 30 has an imaging component for capturingimage data for the gaming table surface. The gaming table surface hasdefined betting areas, and the imaging component captures image data forthe betting areas. A transceiver transmits the captured image data overa network and receives calibration data for calibrating the betrecognition device 30 for the betting areas. Bet recognition device 30may also include a sensor component and a scale component, in someembodiments. The image data may, for example, focus on a particularregion of interest or regions of interest that are within the field ofview of the sensor component.

In some embodiments, the bet recognition devices 30 are hardwareelectronic circuitry that are coupled directly in or indirectly to agaming surface. In some embodiments, the bet recognition device 30 isintegrated into the gaming surface. The bet recognition device 30 may beprovided as a retrofit for existing gaming surfaces (e.g., screwed in,provided as part of a chip tray).

The bet recognition devices 30 may further include illuminatingcomponents or other peripheral components utilized to increase theaccuracy of the bet recognition. For example, an illuminating bar may beprovided that provides direct illumination to chip stacks such that theimaging component is more able to obtain consistent imagery, which mayaid in processing and/or pre-processing of image data. Anotherperipheral component may include the use of pressure sensitive sensorsat the betting area to denote when there are chips present in thebetting area, and in some embodiments, the weight of the chips (e.g.,which can be used to infer how many chips, which can be cross-checkedagainst the image data).

The bet recognition device 30 may have one or more processors andcomputational capabilities directly built into the bet recognitiondevice 30. In some embodiments, these computational capabilities may belimited in nature, but may provide for image pre-processing featuresthat may be used to improve the efficiency (e.g., file-size, relevancy,redundancy, load balancing) of images ultimately provided to a backendfor downstream processing. The bet recognition device 30 may alsoinclude some storage features for maintaining past data and records.Some implementations provide for a very limited window of processingtime (e.g., fast betting rounds or game resolution), and thepre-processing aids in speeding up computation so that it may beconducted in a feasible manner in view of resource constraints.

In some embodiments, the bet recognition device 30 contains multiplephysical processors, each of the physical processors associated with acorresponding sensor and adapted to track a particular bet area. In suchan embodiment, the system has increased redundancy as the failure of aprocessor may not result in a failure of the entirety of bet recognitioncapabilities, and the system may also provide for load balancing acrosseach of the physical processors, improving the efficiency ofcomputations. Each sensor may be tracked, for example, using anindividual processing thread.

The system includes a game monitoring server 20 with a processor coupledto a data store 70. In some embodiments, the game monitoring server 20resides on, near or proximate the gaming surface or gaming table. Forexample, the game monitoring server 20 may include a computing systemthat is provided as part of a dealer terminal, a computer that isphysically present at a gaming station, etc.

The game monitoring server 20 processes the image data received from thebet recognition devices 30 over the network to detect, for each bettingarea, a number of chips and a final bet value for the chips. The gamemonitoring server 20 may also process other data including sensor dataand scale data, as described herein.

The game monitoring server 20 is configured to aggregate game activitydata received from bet recognition devices 30 and transmit commands anddata to bet recognition devices 30 and other connected devices. The gamemonitoring server 20 processes and transforms the game activity datafrom various bet recognition devices 30 to compute bet data and toconduct other statistical analysis.

The game monitoring server 20 may connect to the bet recognition devices30 via bet recognition utility 40. The bet recognition utility 40aggregates image data received from multiple bet recognition devices 30for provision to the game monitoring server 20 in a tiered manner. Insome example embodiments, game monitoring server 20 may connect tomultiple bet recognition utilities 40.

Each bet recognition device 30 may be linked to a particular gamingtable and monitor game activities at the gaming table. A gaming tablemay be retrofit to integrate with bet recognition device 30. Betrecognition device 30 includes an imaging component as described herein.In some embodiments, bet recognition device 30 may also include sensorsor scales to detect chips.

Bet recognition utility device 40 connects bet recognition devices 30 tothe game monitoring server device 20. Bet recognition utility 40 may actas a hub and aggregate, pre-process, normalize or otherwise transformgame activity data, including image data of the gaming tables. In someembodiments, bet recognition utility 40 may relay data. Bet recognitionutility 40 may be linked to a group of gaming tables, or a location, forexample.

Bet recognition utility device 40, for example, may be a backend servercluster or data center that has a larger set of available computingresources relative to the game monitoring server 20. The bet recognitionutility device 40 may be configured to provide image data in the form ofextracted and/or compressed information, and may also receiveaccompanying metadata tracked by the bet recognition device 30, such astimestamps, clock synchronization information, dealer ID, player ID,image characteristics (e.g., aperture, shutter speed, white balance),tracked lighting conditions, reference illumination settings, amongothers.

This accompanying metadata, for example, may be used to providecharacteristics that are utilized in a feedback loop when bet outcomesare tracked. For example, the type of image characteristics or referenceillumination characteristics of the bet recognition utility device 40may be dynamically modified responsive to the confidence and/or accuracyof image processing performed by the bet recognition utility device 40.In some embodiments, the bet recognition utility device 40 extracts fromthe image data a three-dimensional representation of the betting andmaybe used to track not only betting values but also chip positioning,orientation, among others. This information may, for example, be used totrack patterns of betting and relate the patterns to hand outcomes, theprovisioning of complimentary items, player profile characteristics,etc.

The system may also include a front end interface 60 to transmitcalculated bet data, and receive game event requests from differentinterfaces. As shown in FIG. 2, front end interface 60 may reside ondifferent types of devices. Front end interface 80 may provide differentreporting services and graphical renderings of bet data for clientdevices. Graphical renderings of bet data may be used, for example, byvarious parties and/or stakeholders in analyzing betting trends. Gamingfacilities may track the aggregate amounts of bets by account,demographic, dealer, game type, bet type, etc. Dealers may utilizebetting information on a suitable interface to verify and/or validatebetting that is occurring at a table, pit bosses may use the bettinginformation to more accurately determine when complementary items shouldbe dispensed and provided, etc.

Front end interface 60 may provide an interface to game monitoringserver 20 for end user devices and third-party systems 50. Front endinterface 60 may generate, assemble and transmit interface screens asweb-based configuration for cross-platform access. An exampleimplementation may utilize Socket.io for fast data access and real-timedata updates.

Front end interface 60 may assemble and generate a computing interface(e.g., a web-based interface). A user can use the computing interface tosubscribe for real time game event data feeds for particular gamingtables, via front end interface 60. The interface may include a firstwebpage as a main dashboard where a user can see all the live gamingtables and bet data in real time, or near real time. For example, themain dashboard page may display bet data, hand count data, player countdata, dealer information, surveillance video image, and so on. Bet datamay include, for example, total average and hourly average bets perhand, player or dealer, per hour bet data for each gaming table in realtime, and so on. The display may be updated in real-time.

The interface may include a management page where management users canperform management related functions. For example, the interface mayenable management users to assign dealers to inactive gaming tables orclose live gaming tables. An on and off state of a gaming table may senda notification to all instances of the interface. If a user is on themonitor management page when a new gaming table is opened, the user maysee the live gaming table updated on their display screen in real-time.The management page may also shows surveillance images of each gamingtable, and other collected data. The surveillance images may be used ortriggered upon detection of particular patterns of bet data at a gamingtable, for example.

Front end interface 60 may include a historical data webpage, which maydisplay historical bet data of a selected gaming table. It may allow theuser to browse the historical bet data by providing a date rangeselecting control. The bet data may be organized hourly, daily, monthly,and so on depending on the range the user chooses. The bet data alongwith the hand data and a theoretical earning coefficient may be used toestimate the net earnings of the gaming table over the selected dateperiod.

A server and client model may be structured based on receiving andmanipulating various sorts of game event data, such as hand count data,betting data, player data, dealer data, and so on. The interface may beexpanded to process other types of game data such as average bets perhands on a table. Bet data can be displayed on the monitor or managementpage in an additional graph, for example. The date range selection toolmay be used for analyzing the added data along with the bet data.Similarly, the main dashboard may show real-time statistics of both thebet data and the additional game data.

In some embodiments, the bet recognition utility device 40 may receiveactivation/deactivation signals obtained from various external devices,such as an external shoe, a hand counting system, a player accountregistration system, a pit boss/employee manual triggering system, etc.These external devices may be adapted to transmit signals representativeof when a betting event has occurred or has terminated. For example, aspecially configured dealer shoe may be operated to transmit signalswhen the dealer shoe is shaken, repositioned, activated, etc., or a handcounting system may be interoperating with the bet recognition utilitydevice 40 to indicate that a new round of betting has occurred, etc. Insome embodiments, betting may be triggered based on the particular gamebeing played in view of pre-defined logical rules establishing whenbetting rounds occur, when optional betting is possible (e.g.,side-bets, insurance bets, progressive bets), etc.

The system 10 may also integrate with one or more third party systems 50for data exchange. For example, a third party system 50 may collectdealer monitoring data which may be integrated with the bet datagenerated by game monitoring server device 20. As another example, athird party system 50 may collect player monitoring data which may beintegrated with the bet data generated by game monitoring server device20.

FIG. 1B is an example block schematic 100B illustrative of somecomponents of a bet recognition system 200, according to someembodiments. The components shown are for example only and may reside indifferent platforms and/or devices. The system 200 may include, forexample, an imaging component 202 including one or more sensors todetect and/or obtain image data representative of betting areas. Theimaging components 202 may be, for example, cameras, sensors, and maycollect image data in the form of video, pictures, histogram data, invarious formats. The image data may have particular characteristicstracked in the form of associated metadata, such as shutter speeds,camera positions, imaging spectra, reference illuminationcharacteristics, etc. In some embodiments, the imaging components mayprovide an initial pre-processing to perform preliminary featurerecognition, optical character recognition, etc. For example, the gamingsurface may have visual indicators which may be tracked as referencemarkers by the imaging components (e.g., optical position markersindicative of betting areas where bets may be placed).

An image processing engine 204 may be provided that is configured toreceive the images and to extract features from the images. In someembodiments, the image processing engine 204 segments and/orpre-processes the raw image data to remove noise, artifacts, and/orbackground/foreground imagery. For example, the image processing engine204 may be configured to visually identify the pixels and/or regions ofinterest (e.g., by using a combination of depth data and similarity/sizeinformation) regarding the chips. Specific stacks of chips may beidentified, along with their constituent chips. The chips may have“bounding boxes” drawn over them, indicative of the pixels to be usedfor analysis. Similarly, in some embodiments, “bounding boxes” are drawnover entire stacks of chips. The image processing engine 204 may extractfeatures from the bounding boxes and, for example, create a compressedtransform representative of a subset of the image information. Forexample, in some embodiments, various vertical, horizontal, or diagonallines of information may be drawn through a determined stack of chips,and samples may be obtained through tracking the image pixels proximateto and/or around a determined centroid for each of the chips.

In some embodiments, to account for variations in markings (e.g.,vertical stripes), the pixels (e.g., horizontal pixels) estimated tocomprise a particular chip are blurred and/or have other effectsperformed on them prior to extraction such that the centroid and itssurrounding pixels are representative of the chip as a whole.

The image processing engine 204 may also extract out a particular heightof the chips, and this information may be utilized to determine thegeneral size and/or makeup of the stack of chips. For example, knowledgeof the chip stack, distance, and height of specific chips may permit forthe segmentation of pixel information on a per-chip basis.

The image recognizer engine 206 may obtain the extracted and compressedinformation from the image processing engine 204, applying recognitiontechniques to determine the actual chip value for each chip in therelevant region of interest. As the image recognizer engine 206 receivesa set of features, the image recognizer engine 206 may be configured toutilize a classifier to determine how well the feature set correspondsto various reference templates. In some embodiments, the classifierprovides both an estimated value and a confidence score (e.g., a marginof error indicative of the level of distinction between potential chipvalue candidates). Where the chip value cannot be reliably ascertainedthrough the reference templates, a notification may be provided toeither request re-imaging with varied characteristics, or to generate anerror value. For example, features may be poorly captured due to changesin ambient lighting and/or environmental shadows, and the notificationfrom the classifier may control a reference lighting source to activateand/or modify illumination to potentially obtain a more useful set ofimage features.

In some embodiments, the image recognizer engine 206 may dynamicallyprovision computing resources to be used for recognition. For example,if the image recognizer engine 206 identifies that a larger amount ofprocessing will be required in view of a large volume of poor qualityimage data, it may pre-emptively request additional processing resourcesin view of a requirement to complete processing within a particulartimeframe. Conversely, in some embodiments, where image data is ofsufficiently high quality to quickly and accurately conclude that a chipis a particular type of chip, processing resources may be freed up.

A rules engine subsystem 208 may be provided in relation toclassification of chip image data/features to chip values. The rulesengine subsystem 208 may, for example, include tracked linkages andassociations that are used by the classifier to determine a relationshipbetween a particular reference feature set. In some embodiments, therules engine subsystem 208 includes weighted rules whose weightsdynamically vary in view of updated reference feature sets or accuracyfeedback information (e.g., indicated false positives, false negatives,true positives, true negatives), among others. The rules enginesubsystem 208 may also include logical processing rules that controloperation of various characteristics of the classifier, the referenceillumination, processing characteristics, etc.

A game monitoring engine 210 may obtain the tracked chip/bet values foreach bet, for example, from a plurality of imaging components 202,processing engines 204 and/or recognizer engines 206, and maintain aninventory of betting data, which may be stored in data storage 250. Thegame monitoring engine 210 may be adapted to provide real ornear-real-time feedback, and also to perform various analyses (e.g.,overnight processing). The game monitoring engine 210 may identifypatterns from combining bet tracking data with other data, such asplayer profile information, demographics, hand counting information,dealer tracking information, etc.

An administrative interface subsystem 212 may be provided foradministrative users to control how the system operates and/or torequest particular analyses and/or reports. A user interface subsystem214 may provide, for example, various graphical interfaces forunderstanding and/or parsing the tracked bet recognition data. Thegraphical interfaces may, for example, be configured to generatenotifications based on tracked discrepancies, etc. The variouscomponents may interoperate through a network 270.

FIG. 99 is an example block schematic illustrative of some components ofa bet recognition system 200, according to some embodiments. Thecomponents shown are for example only and may reside in differentplatforms and/or devices. As depicted in the example embodiment, betrecognition system 200 includes imaging component 202, image processingengine 204, image recognizer engine 206, game monitoring engine 210,rules engine subsystem 208, administrative interface subsystem 212, datastorage 250, user interface subsystem 214, external databases 252, andnetwork 270 (or multiple networks).

As depicted in the example embodiment, imaging component 202 isconfigured to connect to image processing engine 204, for example, toprovide captured images for processing. Image processing engine 204 isconfigured to connect to image recognizer engine 206, for example, toprovide processed images or extracted and compressed information inorder for recognition techniques to be applied. The image recognizerengine 206 receives a set of features, and the image recognizer engine206 may be configured to utilize a classifier to determine how well thefeature set corresponds to various reference templates. Each of imagingcomponent 202, image processing engine 204, image recognizer engine 206,game monitoring engine 210, and user interface subsystem 214 can beconfigured to connect with data storage 250 to provide or retrieve data.Data storage 250 is configured to store data received and retrieve datafrom one or more external databases 252.

As depicted in the example embodiment, game monitoring engine 210 isconfigured to connect with rules engine subsystem 208, for example, toprovide data allowing rules engine subsystem 208 to track linkages andassociations that are used by a classifier to determine a relationshipbetween a particular reference feature set. The data can be, forexample, tracked chip/bet values for each bet in a set of bets or in agame. Rules engine subsystem 208 can be configured to provide feedbackto game monitoring engine 210, such as acknowledgement of receipt ofdata.

As depicted in the example embodiment, administrative interfacesubsystem 212 is configured to connect with rules engine subsystem 208,for example, to allow users to control how the system operates and/or torequest particular analyses and/or reports. Administrative interfacesubsystem 212 can receive from rules engine subsystem 208 data topresent reports or analyses, for example, to a user. User interfacesubsystem 214 is configured to connect with administrative interfacesubsystem 212, for example, to receive configuration, permissions,and/or security data. This may allow for an administrator to tailorgraphical interface elements generated by user interface subsystem 214and presented to a user.

As depicted in the example embodiment, user interface subsystem 214 andadministrative interface subsystem 212 are configured to interoperateover a network (or multiple networks).

In some example embodiments, game monitoring server 20 may connectdirectly to bet recognition devices 30. FIG. 2 illustrates a blockdiagram 200 of another system for monitoring game activities at gamingtables according to some embodiments. System may include bet recognitiondevice 30 at gaming table with defined bet areas 34 on the gaming tablesurface. In this example, bet recognition device 30 directly connects togame monitoring server 20 to provide image data for the gaming tablesurface and the bet areas 34.

FIG. 3 illustrates a block diagram 300 of a further system formonitoring game activities at gaming tables according to someembodiments involving betting data and hand count data. Card gameactivities may generally include dealing card hands, betting, playingcard hands, and so on. Each player, including the dealer and customers,may be dealt a card hand. For a card game, each active player may beassociated with a card hand. The card hand may be dynamic and changeover rounds of the card game through various plays. A complete card gamemay result in a final card hand for remaining active players, finalbets, determination of winning card hands amongst those active players'hands, and determination of a winning prize based on winning card handsand the final bets. At different rounds or stages of the game differentplayers make bets by placing chips in bet regions on the gaming tablesurface.

Bet recognition device 30 and hand count device 32 may be integrated ateach gaming table for capturing image data for bets and counting thenumber of card hands played at the particular gaming table. Hand countdevice 32 is another example of a game monitoring device. A player mayhave multiple card hands over multiple games, with different betsassociated with hands. Hand count device 32 may count the number ofhands played at a gaming table, where the hands may be played by variousplayers. Bet recognition device 30 may collect image data for server 20to calculate bet data for different hands and players.

Hand count device 32 may determine a player hand count may be over atime period. Bet recognition device 30 may determine bet data over atime period, using timestamps, for example. Server 20 may correlate handcount and bet data using timestamps or time periods, for example. Theinformation may be stored on data store 70, and presented on front enterinterface 60.

Bet recognition device 30 may associate bet data with a particulargaming table, dealer, customers, geographic location, subset of gamingtables, game type, and so on. Similarly, hand count device 32 mayassociate hand count data with a particular gaming table, dealer,customers, geographic location, subset of gaming tables, game type, andso on. For example, bet data may be associated with a timestamp andgaming table identifier to link data structures for further dataanalysis, processing and transformation.

Metadata is collected alongside image data and may be associated (e.g.,using pointers, labels, metadata tags) with the image data to indicateadditional information, such as checksums (e.g., for redundancy andimmutability), timestamps, player information, hand count information,bet round information, lighting conditions, reference lightingcharacteristics, confidence score associated with image data, sensors inuse, processor in use, etc.

Image data, along with other metadata may be encapsulated in the form ofinformation channels that may be use for transmission and/or otherwiseencoded. In some embodiments, 10 or more channels of information areprovided by the bet recognition device 30, and the channels may include,for example, image data taken with different color balances andparameters, image data from different sensors, metadata, etc.

Each bet recognition device 30 may transmit image data or other bet datato bet recognition utility 42 for provision to game monitoring server20. Each hand count device 32 may transmit hand count data from a sensorarray to hand count utility 42 for provision to game monitoring server20. Further details on hand count device 32 and game monitoring server20 for calculating hand count data is described in U.S. ProvisionalApplication No. 62/064,675 filed Oct. 16, 2014 the entire contents ofwhich is hereby incorporated by reference.

Hand count device 32 may include sensors, such as for example lasersensors with optical emitters and receivers. Laser sensors, instead ofother types such as ambient light sensors, may be advantageous to reducethe effect of lighting in the environment, to not require special tabletop felt material, to waterproof the device, and so on. Ambient lightsensors may not work well if a part of the table is not well lit, asthose types of sensors are looking for darkness for object detection.Hand count device 32 may use optical receiver and emitter sensors thatlook for light for object detection. Additional types of sensors includeradio frequency and optics. The sensors may be organized to form asensor array. Hand count device 32 may further include an infraredreceiver and infrared emitter or transmitter for electronic dataexchange. The sensors are particularly configured and positionedrelative to the play area and bet area on the gaming table. For example,a sensor array may be positioned proximate to the card play area and betarea. The device may be configured to provide a particular distancebetween sensor and card play area or bet area, such as a one centimeterdistance, for example.

Bet recognition device 30 may similarly retrieve image data captured byimaging component. Hand count device 32 may receive power and retrievedata off of sensors used for monitoring game activities. Both hand countdevice 32 and bet recognition device 30 generate game activity data(which may also be referred to herein as game event data) for provisionto game monitoring server 20. Game activity data may include hand countdata events, such as hand start event data and hand stop event data.Hand start event data indicates the start of a new hand. Hand stop eventdata indicates the end of a hand. Together with timestamps these valuesmay be used to compute hand duration and other data values. Bet data mayalso be linked by timestamps. The sensors of hand count device 32 may bepositioned on the gaming table to detect card hand activities andtrigger hand start events and hand stop events. The sensors may deliverreal-time data regarding card play activity, including hand start eventdata and hand stop event data. The imaging components may also deliverreal-time image data regarding bet activities. The imaging component ofbet recognition device may be mounted or integrated into gaming table tocapture real-time image data for bet areas on the gaming table surface.

In some embodiments, the clocks of the bet recognition device 30, thehand count device 32, game monitoring server 20 are synchronizedtogether to ensure that data is readily interpretable regardless ofsource.

Bet recognition device 30 may be configured with particular triggerevents, such as detection of chips or objects in defined bet areas onthe gaming table by sensors. The trigger events may trigger imagingcomponent to capture image data for calculating bet values for thechips. A timing or threshold value may be set off to triggertransmission of game event data used to calculate bet data and countcard hands. An example trigger may be sensor activation for a thresholdvalue, for example two, three or four seconds. Another example triggermay be sensor deactivation for a threshold value.

Game activity data may include bet data, player count data and handcount data, which may be valuable for casinos for security, management,and data analytics. For example, a casino may determine a link between agame and a dealer, and also a dealer and a customer, through the betdata, the hand count data and the player count data. A casino mayprovide real-time compensation to players using the bet data, handcount, and player count data. Accordingly, the systems, devices andmethods in accordance with embodiments described herein may providevarious levels of granularity and specificity for game activity data,using the bet data, hand count data, player count data, and othergenerated game activity data values. There may further be third partyplayer tracking and/or dealer tracking data 50 that may be utilized inrelation to performing analysis and reporting.

A gaming table includes one or more bet areas. FIGS. 4A-4C illustrates aschematic diagram of bet areas 34 monitored by a bet recognition device30 according to some embodiments.

As illustrated in FIGS. 4A-4C, a challenge with tracking betting andchips is the ability to obtain sufficient quality and resolution toaccurately track bets. FIG. 4A is an overhead or elevational top view400A, according to some embodiments. FIG. 4B is a perspective view 400B,according to some embodiments. FIG. 4C is an overhead or elevational topview 400C in relation to a camera system 30, according to someembodiments. Bets 402 may be placed in a betting area 34 on a gamingtable, and for example, betting areas may be demarcated through the useof machine-vision interpretable boundaries, etc. The bets may includevarious chips, and the chips may have different values attributed to thechips. The chips may be placed in one or more stacks within the field ofview of the camera system 30.

These boundaries, for example, may appear to be a single visual ring toa player, but in some embodiments, layers of different boundaries (e.g.,as different rings) may be utilized to more granularly indicate slightdifferences in positioning of chips. For example, boundaries that areonly visible in the infrared or ultraviolet lighting may be used, andthese may be tracked by machine-vision sensors to demarcate where thebetting area begins, ends, different parts of a betting area, etc. Forexample, such granular boundaries may be helpful where small differencesin depth, positioning, etc. may impact the accuracy of such a system.Visual and/or other types of optical markers may be used to serve asreference areas for depth calculations

While some other systems have utilized overhead cameras positioned overa table or based on tracking images captured from overhead securitycameras, these systems have had difficulties obtaining sufficiently highquality images of chips placed in betting areas to be able to accuratelyand responsively track bet counting. For example, using an overheadcamera may lead to an inconsistent number of pixels being used to trackeach chip, the number of available pixels being limited due to theobstruction caused by chips being placed on one another (e.g., anoverhead camera directly above a stack of chips may not be able toadequately identify chips underneath the top chip of a stack, or if itis placed at an overhead some distance away, the system may not have agood view of the individual chips within the stack as there may eitherbe obstructions or the specific angle of the chips may cause undesirableshadowing effects. For example, depending on a camera's ability toobtain images, chips deep in a stack of chips may all appear to be blackas the chips in the stack end up casting shadows on one another.Perspective views of chips may computationally difficult to analyze inview of the required transformations to obtain a representative set ofpixels.

Similarly, it may be difficult to account for variations of ambient andenvironmental lighting that may be illuminating the chips themselves.Where differences in illumination intensities are utilized to track chipvalues and distances, such variations may reduce the accuracy ofreadings or provide false positive/false negative readings.

In some embodiments, imaging components (e.g., cameras) are placed andpositioned to have a substantially horizontal sensor angle when viewingthe chips, a depiction of which is provided at FIG. 4B. Substantiallyhorizontal may mean substantially parallel to a plane of the gamingsurface.

The imaging components may be adapted such that the imaging component isdirected towards the betting areas from or near the perspective of adealer. Such a configuration may be helpful in ensuring that the chipsare less obstructed, and provide a sufficient view of the sidewalls ofthe chips. An “offset angle” may be provided where the imagingcomponents, while “looking” substantially parallel at the sidewalls ofthe chips, due to the stacked nature of chips, may aid in obtaining asmany pixels as possible.

As described, the imaging component angle may be important to ensurethat as many pixels of information can be extracted from amachine-vision image that are representative of chips. The imagingcomponent itself may also require to be off-set from the gaming surface(e.g., at a particular altitude or height) such that the sensing is notblocked by the presence of objects on the gaming surface, such asplaying cards, dice, markers, etc. For example, a card may be curled ata corner, and a sensor placed directly horizontal and in contact withthe gaming surface may end up being obstructed by the cards (and thusunable to read the value of the chips). The horizontal angle, forexample, may be an angle between −5 to 5 degrees, and the altitude maybe between 0.2 cm to 1.0 cm. While the image obtained may be direct forsome chips, there is nonetheless some angle for chips that are at thetop or the bottom of the stack.

In some embodiments, the imaging component may be utilized incombination with an illumination strip, the illumination strip (e.g.,lights, infrared lights, ultraviolet lights) providing a “referenceillumination” against the sidewall of the chips.

For example, the illumination strip may be placed above or below theimaging component and may provide illumination in all or a portion ofthe field of view of the imaging component. The illumination providedmay be static (e.g., a regular light) or controlled (e.g., acontrollable light). The illumination characteristics may be modified(e.g., filters applied, the amount of total light controlled, thespectral makeup of the light may change, etc.). The illuminationcharacteristics may be used in various ways, for example, to ensure thatat a minimum number of pixels are able to be captured per chip, toensure that there is constant reference illumination despite changes inambient lighting, etc.

In some embodiments, illumination characteristics are modified inresponse to requests from the system. For example, the system maydetermine that there indeed are chips providing in a particular area,but the system is experiencing difficulty in assessing the value of thechips (e.g., due to environmental, ambient illumination, distortions.

In some embodiments, the imaging component and/or the illumination isprovided on a physical track or pivot and is able to modify viewingangles and/or positions (or both) in response to poor image recognition.For example, at some angles, chips may be covered by shadows (especiallythe bottom chips of a stack) and due to the shadowing, may appear to beindistinguishable from other chips or erroneously recognized. Theclassifier may identify a low level of confidence in the recognition andin response, generate a control signal to move the camera and/or pivotthe camera and/or other sensors, such as depth sensors, etc.

A control system may note that the recognition device is havingdifficulty (e.g., by an increase in error rate, failing to meeting apre-defined threshold of pixels required to make an accuratedetermination) and issue a command control to the illumination device tocontrol the illumination device to more actively “light up” the chips sothat a better picture may be taken for conducting image recognition.

Similarly, bet recognition devices may be designed to operate inenvironments where the amount of environmental and ambient lightingvaries quite frequently. Light may be provided from natural sources(e.g., windows), or from artificial sources. Ambient lighting may occurfrom artificial sources that are incident to the bet recognition device,such as the lights provided on other machines, room lighting, etc. Insome embodiments, a gaming facility may purposefully modify the lightingconditions to impress upon the players a particular ambience or theme.Individuals at the facility may be smoking, casting shadows, etc.

These variations may significantly impact the ability of the system toperform bet recognition. A commercial consideration as to how the systemfunctions is the ability to operate the system in a variety of differentenvironments having different lighting conditions. For example, a betrecognition system may require some level of portability as the systemmay be moved around a gaming facility over its lifetime, or sold and/ormoved between different gaming facilities.

In some embodiments, the aspect ratio associated with the imagingcomponent may be a factor for consideration. For example, if the imagingcomponent was a 1080p camera, it means it has more pixels horizontallythan vertically, so the extra resolution in the width is more valuablein measuring the thickness of the chip. Rotating from a landscapeorientation to a portrait orientation would allow for more resolutionfor distinguishing chips from one another within a stack, potentiallyoffering more detail to for downstream processing.

In some embodiments, an illumination strip provides the referenceillumination, and the reference illumination may be provided in asubstantially horizontal view relative to the sidewalls of the chips.The reference illumination may, relative to overhead camera systems,provide more direct and relatively unobstructed illumination to the chipsidewalls, making any machine-vision interpretable markings more visibleand easy to distinguish. As an example in the context of machine vision,particular colors may be difficult to distinguish from one another(e.g., red from pink), and similarly, striped markings may also bedifficult to process as poor lighting may impact the ability todetermine how thick a line is, etc. This problem may be particularlyexacerbated if the machine-vision is not operating in the same rangewavelengths as human vision, for example, if the machine vision operatesin infrared, ultraviolet ranges, monochromatic ranges, etc.

The reference illumination may be provided proximate to or substantiallyat the same location as the imaging components. For example, thereference illumination may be provided in the form of an illuminationstrip running across a sensor housing. In some embodiments, thereference illumination is provided in the form of spaced-apart lightsources.

Accordingly, in some embodiments, the reference illumination inaccordance with control signals such that the reference illuminationcharacteristics (intensity, spread, spectral makeup, etc.) may bemodified and monitored to dynamically adjust and/or control forvariations from light provided from other sources For example, controlsignals may be provided, which when processed by the illumination strip,the illumination strip changes an intensity of the referenceillumination based at least on ambient lighting conditions. The controlsignals may be adapted to implement a feedback loop wherein thereference illumination on the one or more chips is substantiallyconstant despite changes to the ambient lighting conditions.

In some embodiments, rather than, or in combination with changing thereference illumination to provide a constant lighting condition, thereference illumination is adapted to monitor a confidence levelassociated with the bet recognition processing from machine-visionimages that are provided to a backend system. For example, if thebackend image processing system indicates that there are significantaccuracy and/or confidence issues, the backend image processing systemmay be configured to generate a control signal requesting modificationsto the reference illumination relative to the chips themselves. Outcomesmay be monitored, for example, by using a feedback loop, and controlledsuch that an optimal amount of reference lighting is provided. In someembodiments, the reference illumination is not constant, but is ratheradjusted to ensure that a sufficiently high level of confidence isobtained during image processing. In some embodiments, referenceillumination may be provided in a strobe fashion and/or otherwiseintermittently used when image processing capabilities are impacted(e.g., a transient shadow passes by, the chips are momentarilyobstructed by the hand of a player or the dealer, etc.).

The reference illumination, to save energy, may, in some embodiments, becontrolled such that it can be turned on whenever additionalillumination is required.

Embodiments described herein provide a game monitoring server 20configured to calculate a red green blue (RGB) Depth Bounding Area for agaming table to calibrate the corresponding bet recognition device 30.

Game monitoring server 20 and bet recognition device 30 calibrates eachbet area to ensure that only the chips in the bet area are counted, andnot chips in other areas of the gaming table that are not in play. Thebet area may be defined in response to input received at front endinterface 60 providing a graphical display of the gaming table surfaceand using an input device (e.g. a keyboard and mouse) to define aregion. As another illustrative example, the bet area may also bedefined by positioning chips in the bet area and nothing else on thegaming table.

Game monitoring server 20 and bet recognition device 30 mayautomatically implement bet area calibration by scanning the gamingtable and detecting any chips on the gaming table surface. If the chipson the gaming table are directly inside the bet area then gamemonitoring server 20 will automatically record xyz coordinate values forthe detected chips.

The game monitoring server 20 may be configured for performing varioussteps of calibration, and the calibration may include developing anarray of reference templates in relation to the particular set up of agaming surface or gaming table. For example, the reference templates mayinclude what chips “look like” at a particular table in view of usualgameplay settings, etc. Further, the reference templates may tracklighting conditions across the span of a day, a season, in view ofupcoming events, nearby light sources (e.g., slot machines), etc. Newtemplates may be provided, for example, when new chips or variations ofchip types are being introduced into circulation at a particular gamingfacility. In some embodiments, such introduction of new chips mayrequire a machine-learning training phase to be conducted to build areference library.

The calibration may be conducted, for example, on an empty table todetermine where the betting areas should be, where any demarcationsexist, etc. The calibration may be used to “true up” color settings,lighting conditions, distances, etc. For example, the referencetemplates may be indicative of how many pixels are generally in a chipin a first betting area, based on their position on a stack of chips,etc. The calibration may also track the particular height of chip stacksbased on how many chips are in the stacks and what kind of chips are inthe stack. These reference values may be stored for future use duringbet recognition, and may be compressed such that only a subset offeatures are stored for reference. The subset of features stored, forexample, may be utilized in a pattern recognition and/or downstreammachine-learning approach where relationships are dynamically identifiedbetween particular features and recognized bets.

The calibration may be conducted with reference games and betting, andtracked against manual and/or semi-manual review to determine accuracyand features for extraction, and characteristics associated with the betrecognition may be modified over time. For example, theprocessing/pre-processing steps may be modified to change definitions ofbet areas, bounding boxes, what image features are considered backgroundor foreground, how lighting needs to be compensated for in view ofchanging lighting conditions, transient shadowing, etc.

Embodiments described herein provide a game monitoring server 20configured to monitor betting activities by calculating RGB and DepthData for chips detected within bet areas of the gaming table.

The game monitoring server 20 may be configured to generate anelectronic representation of the gaming table surface. The gamemonitoring server 20 is configured to process the captured chip dataimages by segmenting the background chips and other data from the gamingtable images relative to the distance from camera component of the betrecognition device 30, or relative to the position on the gaming table.The bet recognition device 30 may only capture and transmit portions ofthe image data relating to the chip stack itself to the game monitoringserver 20 via bet recognition utility 40. In accordance with someembodiments the game monitoring server 20 or bet recognition utility 40receive image data for the gaming table surface and perform processingoperations to reduce the image data to portions of the image datarelating to the chip stack itself. The game monitoring server 20implements image processing to transform the image data in order todetect the number of chips in the betting area and ultimately the finalvalue of the chips.

In some embodiments, the electronic representation of the gaming tablesurface is used as a streamlined approach to extracting informationrelevant to the bet recognition. For example, the gaming table surfacemay be represented in two-or three dimensional space and used forcoordinate positioning. There may be defined bet areas that are providedbased on position, etc. and in some embodiments, the actual bet areasmay include further markings that may or may not be visible to humanplayers that are used for machine vision boundary demarcation and/ordepth calculations. For example, there may be areas indicated toindicate depth (e.g., if a particular boundary is covered, the boundaryis known to be at position (x, y), and therefore a chip stack is atleast around or covering position (x, y).

The game monitoring server 20 may utilize the electronic representationin generating a streamlined set of compressed information that is usedfor downstream analysis, such as for bet recognition, confidence scoretracking, machine learning, etc. For example, the electronicrepresentation may be updated as chips are placed into betting areas andsensed by the sensors. The sensors may track various elements ofinformation associated with the chips and modify the electronicrepresentation to note that, with a particular confidence level, that astack of chips has been placed, the stack of chips having a particularmakeup and value of chips, etc. The game monitoring server 20 may thenextract out only specific features and discard the other information inpreparing a compressed set of information representative of the betsbeing placed on a gaming surface (e.g., only a simple set of depthcoordinates, the estimated make-up of the chips in the stack, confidencevalues associated with how accurately the system considers itsassessments to be, etc.).

Embodiments described herein provide a game monitoring server 20configured to monitor betting activities by calculating depth data forchips detected within bet areas of the gaming table. Depth data can becaptured with a variety of different processes using different imagingcomponents. For example, an imaging component may include stereo cameras(e.g., RGB cameras) mounted in different positions on the gaming tableto capture image data for the betting areas. An imaging component withstereo cameras may have two or more black/white or RGB cameras, forexample. As another example, an imaging component may include a depthaware camera using Infrared (IR) and Time-Of-Flight (TOF). An imagingcomponent with depth cameras may use IR TOF or IR projection ofstructured light or speckled pattern.

Depth data may be an important output in relation to machine-visionsystems. For example, the distance from a chip may indicate how manyavailable pixels would make up chips in an image of chips, etc. Thenumber of available pixels may determine how a bounding box may be drawn(e.g., dimensions) around a chip, a stack of chips, etc., and in someembodiments, may be a factor in automatic determinations of confidencescores associated with machine-vision estimations of the values of thechips (e.g., if there are only 12 pixels available due to the stackbeing far away for a particular chip, and the pixels are impacted bypoor lighting conditions and partial obstructions, the confidence scoremay be particularly low, especially if the chip has markers that aredifficult to discern in poor lighting conditions).

Depth data may be generated based on, for example, tracking parallaxeffects (e.g., by moving a single sensor), stereoscopic effects (e.g.,by comparing parallax in two different cameras), pressure sensors in thebetting areas, range finding radar (e.g., Doppler radar), UV lightdispersion/brightness levels, distortion effects, etc.

Where sensors may be obstructed, depth data may be estimated from anaggregated set of captured images over a duration of time. For example,differences between each of the plurality of captured images may becompared to estimate the presence of the one or more chips despite thepresence of the one or more obstructing objects that are partially orfully obstructing the one or more chips from being sensed by the one ormore sensors.

The depth data may be determined in combination with a confidence score.For example, if a chip is particularly far away, there may be a limitednumber of pixels to analyze regarding the chip. The number of pixelsavailable may be further reduced if lighting conditions are poor, thechips are obstructed, there are imaging artifacts, etc., andaccordingly, a lower confidence score may be presented.

FIGS. 5 to 7 illustrate example images taken from a bet recognitiondevice mounted on a gaming table according to some embodiments. Theimage data for the images may include depth data taken from atable-mounted depth camera. The example images illustrate the table andbet area rendered in three-dimensions (3D). The image data definesstacks of chips in the bet areas in 3D defined using X, Y, and Zcoordinate values. The game monitoring server 20 may represent a gamingtable surface as a model of X, Y and Z coordinate values includingbetting areas.

As depicted in FIG. 5, the sensors may take images 500 of the gamingsurface, including bet area 502/502′, cards 508/508′, stacks of chips504/504′/506/506′, etc. As noted in FIG. 5, there may be some chips inthe foreground 504/504′, some in the background 506/506′, there may beother background images, etc. The images may be taken in human visibleand/or non-human visible wavelength ranges. For example, the images mayutilize infrared, ultraviolet, and accordingly, some wavelengths may bemore prominent than others. Another image 600 is depicted in FIG. 6,having bet area 602/602′, cards 608/608′, stacks of chips604/604′/606/606′, etc. As noted in FIG. 6, there may be some chips inthe foreground 604/604′, some in the background 606/606′.

FIG. 7 is a compressed representation 700, wherein non-chip imagery hasbeen filtered out, and the image primarily consists of images of stacksof chips 702, 704, and 706. Filtering techniques, for example, includethe use of edge detection algorithms (e.g., difference of Gaussians).The representation 700 may be compressed such that chips are detectedamong other features, so that various regions of interest can beidentified. In some embodiments, representation 700 is combined withdepth data so that background and foreground chips may be distinguishedfrom one another. For example, chips that may be within a betting areaindicative of a bet may also have chips that are in the background(e.g., chips that the player has in the player's stacks that are notbeing used for betting). Depth data may be used to distinguish betweenthose chips that are in the betting area as opposed to those chips thatare out of the betting area.

In some embodiments, the imaging component may include a wide anglecamera. The wide angle camera can used to capture image data for all betareas game monitoring server 20 may implement correction operations onthe image data to remove warping affects. FIG. 8 illustrates an exampleschematic 800 of a bet recognition device 30 with a wide angle camera.

In some embodiments, the imaging component may include three cameras802, 804 and 806 per gaming table. Three cameras may be mounted on agaming table in a configuration in front of the dealer position on thegaming table. Each camera may be dedicated or responsible for two betareas. When a hand has started (e.g., as per a detected hand startevent) each camera may capture image data of their respective bet areasfor transmission to game monitoring server 20. The game monitoringserver 20 stores the captured image data in a central data store 70.When new image data is sent to the data store 70 it may be processed bygame monitoring server 20 using the recognition process describedherein. The processed image data may generate bet data including thenumber of chips in a bet area and the total value of chips in the betarea. Game monitoring server 20 stores calculated bet data at data store70. The bet data may be linked with timestamp which is also recorded atdata store 70. When all image data captured by the three cameras hasbeen processed, image data for the hand played is recorded to the datastore 70. Game monitoring server 20 is operable to correlate the capturehand event data to the bet data to calculate bet values for particularhands.

The cameras of imaging component can be installed into the back bumperlocated between the dealer position and the gaming table. Thisinstallation process simplifies retrofitting existing gaming tablesallowing the casino operator to replace the back bumper without havingto impact the gaming table felt or surface.

FIGS. 8 and 9 illustrate example images of a bet recognition device 30mounted on a gaming table according to some embodiments. Theillustrative example 900 shows three cameras as an imaging component ofthe bet recognition device 30. The cameras are installed into the backbumper of the gaming table. Game monitoring server 20 can stitchtogether image data captured by different cameras to recreate a 3D modelof the table surface using X, Y and Z coordinate values. In some exampleembodiments, the game monitoring server 20 may evaluate image dataindependently for each camera. Game monitoring server 20 uses thecaptured image data to generate a 3D model of the gaming table surface.Game monitoring server 20 processes the image data by isolating imagedata for all bet areas and cropping the image data for all bet areas forfurther processing. Game monitoring server 20 processes the image datafor the bet area in order to find bet value for each bet area and totalbet or chip amount for each bet area.

In some embodiments, the 3D model is utilized to generate arepresentation of where the system interprets and/or estimates the chipsto be. The 3D model may be adapted to extrapolate position informationbased on known sizes and/or dimensions of chips, as the image data mayoften only include an incomplete set of information required to generatea 3D model. Multiple images may be stitched together to derive the 3Dmodel.

FIGS. 10 and 11 illustrate example images of a bet recognition devices30 according to some embodiments. FIG. 10 is an illustration 1000 of anexample bet recognition device 30 including three cameras capturingdifferent portions of the gaming table surface. FIG. 11 is anillustration 1100 that illustrates another example bet recognitiondevice 30 including a camera.

FIG. 12 illustrates a schematic diagram 1202 of another example betrecognition device 30 according to some embodiments. The bet recognitiondevice 30 may include a moving camera according to some embodiments. Alinear bearing can be used alongside a stepper motor to move the camerafrom one side of the gaming table 1204 to the other. This allows for onecamera system to capture image data of the gaming table surface fromdifferent angles (as denoted by the dashed lines). The number of imageframes captured and the interval at which the image frames are capturedcan be defined. The imaging component may be an RGB Camera 30 or two RGBcameras 30′, or one depth camera 30″, for example.

The movement of the camera may be used, for example, to assess depthusing parallax measurements, to stitch together images in generating a3D model, etc.

Game monitoring server 20 implements a chip recognition process todetermine the number of chips for each betting area and the total valueof chips for the betting area. For example, when a new hand is detectedat a gaming table, bet recognition device 30 captures image data of eachbet area on the gaming table surface and sends the captured image datato game monitoring server 20.

In some embodiments, bet recognition device 30 isolates image data foreach bet area from image data for a larger portion of the gaming tablesurface and sends cropped image data only for the bet area to the gamemonitoring server 20. In other embodiments, game monitoring server 20isolates image data for each bet area from image data for a largerportion of the gaming table surface.

Game monitoring server 20 processes each image frame of the capturedimage data and segments image data defining chips for each chip by sizeand color to determine a total number of chips for the betting area.Game monitoring server 20 detects a face value for each chip. Datarecords of data store 70 may link color and size data to face value.Game monitoring server 20 may also implement number recognition on theimage data for chips to detect face value. Game monitoring server 20calculates the total value of the bet by multiplying the number of chipsdetected by the face value of the chips.

FIGS. 13A, 13B and 14 illustrate example images taken from a betrecognition device mounted on a gaming table and processed images aftertransformation by server according to some embodiments. The images1300A, 1300B, and 1400 illustrate segments generated by the gamemonitoring server 20 to determine the number of chips and the face valueof the chips.

Game monitoring server 20 stores the bet data in data store 70. A“bounding box” 1602 is illustrated for chip 1402.

Chips 1300-1314 may be processed by size, and color (e.g., in thisexample, 1302 is a different type of chip than the others), and thesensors and/or imaging components may obtain data, including images invisual spectrum or non-visual spectrum. For example, some markings maybe only shown in infrared or ultraviolet, or may fluoresce in responseto the reference illumination applied to the chips. Texture and shape,in some embodiments, are also tracked based on visually apparentfeatures.

As depicted in FIG. 13B, the images may be processed such that thesidewalls of the chips can be analyzed, by identifying a region ofinterest represented, for example, by the pixels within a “bounding box”1316 and 1316′ around each of the chips. Depending on the amount ofavailable resources, the determination of the region of interest may beconducted at a backend server, or during pre-processing on a devicecoupled to the gaming table, in various embodiments.

Game monitoring server 20 watches for new captured image data for thebet area and processes the new captured images against existing datarecords for bet data stored in data store 70. Chips in the bet area arechecked for size, shape, and color to ensure accuracy. FIGS. 13 and 14illustrate that only the chips in the bet area are detected by the gamemonitoring server 20 bet recognition process. Once processing iscompleted, the game monitoring server 20 sends the bet values of eachbet area or region to the data store 70. In some example embodiments,game monitoring server 20 associates a hand identifier to the bet datavalues. A timestamp may also be recorded with the bet data values storedin data store 70.

Embodiments described herein provide a bet recognition device 30 withboth an imaging component and a sensor component. Bet recognition device30 captures image and sensor data for provision to gaming monitoringserver 20 to calculate bet data.

FIG. 15 illustrates a schematic diagram 1500 of a sensor array devicefor bet recognition device according to some embodiments. A betrecognition device may include a microcontroller, a sensor array networkand connection cables. The microcontroller may run the logic level codefor checking onboard sensors (e.g. sensors integrated into the gamingtables via client hardware devices 30) for pre-defined thresholdstriggering capture of image data to determine bet data. Themicrocontroller may also emulate a serial communication protocol for thehost. The sensor array network may include interconnected sensors thatcan communicate with each other. The sensors may be integrated with agaming table and positioned relative to playing area of the table. Theymay be all connected via the microcontroller and routed accordingly. Aconnection cable may process the digital serial signal and allow thedevice to connect via USB or other protocol (e.g. wireless) to acomputer with a free port. The data may be transmitted via the USB cableor other protocol and may be read by a small utility on the hostcomputer.

FIG. 16 illustrates a schematic graph 1600 of the amplitude of thereceived signal from a sensor and/or imaging component over timeaccording to some embodiments.

The following is an illustrative example measurement setup for a scenepoint. A sensor estimates the radial distance by ToF or RADAR principle.The distance, ρ, is calculated at time T with electromagnetic radiationat light speed C, is ρ=CT. The transmitter emits radiation, whichtravels towards the scene and is then reflected back by the surface tothe sensor receiver. The distance covered is now 2ρ at time T. Therelationship can be written as:

$p = \frac{c\;\tau}{2}$Signal S_(E)(t) may be reflected back by the scene surface and travelsback towards a receiver (back to receiver) and written as:S _(E)(t)=A _(E)[2πf _(mod) t]

Because of free-path propagation attenuation (proportional to the squareof the distance) and the non-instantaneous propagation of IR opticalsignals leading to a phase delay ΔØ. The Attenuated Amplitude ofreceived signal may be referred to as A_(R). The interfering radiationat IR wavelength of emitted signal reaching the receiver may be referredto as B_(R).

Waveforms may be extracted in relation to each chip (e.g., as extractedfrom the available pixels in image data for each channel ofinformation), and these waveforms may represent, for example,information extracted from histogram information, or from other imageinformation. For example, a Fourier transform may be conducted on theimage data separately from extracted histogram information. In someembodiments, a histogram and a Fourier transform are used incombination.

A best-matching waveform approach may be utilized to estimate whichcolor and/or markings are associated with a particular chip. Forexample, each chip may have a corresponding waveform (for each imagechannel) and these may be used, in some embodiments, in aggregate, toclassify the chips based on chip values. In some embodiments, where thedata is not sufficiently distinguishing between different chip values(e.g., poor lighting makes it difficult to distinguish between pink andred), the system may be adapted to provide a confidence score associatedwith how closely matched the waveform is with a reference template. Thisconfidence score, for example, may be used to modify sensorycharacteristics, lighting conditions, etc., so that the confidence scoremay be improved on future image processing. In some embodiments, theinterfaces provided to users may also utilize the confidence score inidentifying how strong of a match was determined between chip images andreference templates. The received signals 1602 and 1604 may be differentfor each type of chip, and the waveforms may be processed through aclassifier to determine a “best match”. As described in someembodiments, the confidence in determining a best match may be based on(1) how well matched the chip is to a reference waveform, and (2) howmuch the chip is able to distinguish between two different referencewaveforms. The confidence score may be used to activate triggers toimprove the confidence score, for example, by automatically activatingreference illumination or requesting additional images (e.g., moving thecamera to get more pixels due to an obstruction, lengthening a shutterspeed to remove effects of motion, temporarily allocating additionalprocessing power to remove noise artifacts).

FIG. 17 is an illustration of a schematic of a game monitoring server 20according to some embodiments.

Game monitoring server 20 is configured to collect game event dataincluding bet data and hand start data and hand stop data, which may bereferred to generally as hand event data. The hand event data may beused to determine a hand count (e.g., the number of hands played byvarious players) for a particular gaming table for a particular periodof time. Bet and hand count data may be associated with a time stamp(e.g., start time, stop time, current time) and table identifier. Thebet data may also be associated with a particular player (e.g. dealer,customer) and a player identifier may also be stored in the datastructure.

For simplicity, only one game monitoring server 20 is shown but systemmay include more game monitoring servers 20. The game monitoring server20 includes at least one processor, a data storage device (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface. Thecomputing device components may be connected in various ways includingdirectly coupled, indirectly coupled via a network, and distributed overa wide geographic area and connected via a network (which may bereferred to as “cloud computing”).

For example, and without limitation, the computing device may be aserver, network appliance, set-top box, embedded device, computerexpansion module, personal computer, laptop, or computing devicescapable of being configured to carry out the methods described herein.

As depicted, game monitoring server 20 includes at least one gameactivity processor 80, an interface API 84, memory 86, at least one I/Ointerface 88, and at least one network interface 82.

Game activity processor 80 processes the game activity data includingimage data, bet data, and so on, as described herein. Each processor 80may be, for example, a microprocessor or microcontroller, a digitalsignal processing (DSP) processor, an integrated circuit, a fieldprogrammable gate array (FPGA), a reconfigurable processor, aprogrammable read-only memory (PROM), or any combination thereof.

Memory 86 may include a suitable combination of computer memory that islocated either internally or externally such as, for example,random-access memory (RAM), read-only memory (ROM), compact discread-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like.

Each I/O interface 88 enables game activity processor 80 to interconnectwith one or more input devices, such as a keyboard, mouse, camera, touchscreen and a microphone, or with one or more output devices such as adisplay screen and a speaker.

Each network interface 82 enables game activity processor 80 tocommunicate with other components, to exchange data with othercomponents, to access and connect to network resources, to serveapplications, and perform other computing applications by connecting toa network (or multiple networks) capable of carrying data including theInternet, Ethernet, digital subscriber line (DSL), coaxial cable, fiberoptics, satellite, mobile, wireless (e.g. Wi-Fi, WMAX), local areanetwork, wide area network, and others, including any combination ofthese.

Application programming interface (API) 84 is configured to connect withfront end interface 60 to provide interface services as describedherein.

Game activity processor 80 is operable to register and authenticate userand client devices (using a login, unique identifier, and password forexample) prior to providing access to applications, network resources,and data. Game activity processor 80 may serve one user/customer ormultiple users/customers.

FIG. 18. illustrates a schematic of a bet recognition device 30according to some embodiments.

As depicted, bet recognition device 30 may include an imaging component90, sensor component 92, processor 91, memory 94, at least one I/Ointerface 96, and at least one network interface 98.

Processor 91 may be, for example, any type of general-purposemicroprocessor or microcontroller, a digital signal processing (DSP)processor, an integrated circuit, a field programmable gate array(FPGA), a reconfigurable processor, a programmable read-only memory(PROM), or any combination thereof.

Memory 94 may include a suitable combination of any type of computermemory that is located either internally or externally such as, forexample, random-access memory (RAM), read-only memory (ROM), compactdisc read-only memory (CDROM), electro-optical memory, magneto-opticalmemory, erasable programmable read-only memory (EPROM), andelectrically-erasable programmable read-only memory (EEPROM),Ferroelectric RAM (FRAM) or the like.

Each I/O interface 96 enables bet recognition device 30 to interconnectwith one or more input devices, such as a keyboard, mouse, camera, touchscreen and a microphone, or with one or more output devices such as adisplay screen and a speaker.

Each network interface 98 enables bet recognition device 30 tocommunicate with other components, to exchange data with othercomponents, to access and connect to network resources, to serveapplications, and perform other computing applications by connecting toa network.

Bet recognition device 30 may also include a scale component. Betrecognition device 30 may monitor chips and cards on the gaming tableusing scales. A scale may be placed underneath the casino table, orunderneath the area on which the chips or cards are placed. The scalemay take measurements during the time periods when no movement of thechips or cards is done. For example, a dealer may and the players mayplace the cards or chips on the table, upon seeing a particular gesture,a scale may read the weight and the system may determine, based on theweight, as well as the monitoring mechanism, the number of cards orchips on the table. The weight reading may be done at a later point, toconfirm that no cards or chips were taken off of the table. The scalemay take measurements of the weight responsive to a command by thesystem. As such, the system may determine when the chips or cards arenot touched by the dealer or the player, thereby ensuring that a correctmeasurement is taken and, in response to such a determination, sending acommand to measure the weight of the chips or cards. As an example,based on the weight and the coloring of the chips, the system maydetermine the present amount of the chips the user may have. This may bean example of game activity.

Using these techniques, the system may monitor and track not only thechips of the dealers but also the chips of the players, may track theprogress of each player, may be able to see when and how each player isperforming, and may also monitor new hands to determine hand count. Thesystem may therefore know the amount of chips gained or lost in realtime at any given time, and may also know the number of cards in eachplayer's hand, and so on.

As described herein, embodiments described herein may provide systems,methods and devices with bet recognition capabilities. Bet recognitiondata may be generated and collected as game event data and may beconnected to hand count data. For example, a hand may involve bettingchips and system may detect chips using bet recognition device 30.

The bet recognition device may capture image data for bet data inresponse to chip detection in a betting region.

The system may involve bet recognition cameras inside of a bumper of thegaming table on the dealer's side. The cameras may be in nearly the samelocation as this may simplify table retrofitting. All of componentsincluding computers for both bet recognition and hand count may beinstalled there.

FIGS. 19 to 39, 51 and 52 illustrate schematic diagrams of betrecognition devices and imaging components according to someembodiments.

Embodiments described herein may implement bet recognition devices andimaging components with different camera positioning options.

For example, a bet recognition device may have an imaging component withone wide-angle lens camera at the back of the table that scans theentire casino table. An example schematic 1900 is shown in FIG. 19including illustrative lines corresponding to a field of view for thecamera.

As another example, a bet recognition device may have an imagingcomponent with three cameras at the back of the table (e.g. left,middle, right), pointing outward towards the player positions.

An illustrative schematic of an imaging component 3200 and 3300 withthree cameras is shown in FIGS. 32 and 33. An example table layout isshown in FIG. 28. Example playing areas for image capture is shown inFIGS. 29 to 31. For example, there may be 21 or 28 playing areas forimage capture for seven players with three or four playing areasallocated per player. The playing areas may be for bets, chips, cards,and other play related objects. As shown, fields of view for cameras mayoverlap such that one or more cameras may capture image datacorresponding to each play area. For example, two cameras may captureimage data corresponding to a play area. As another example, threecameras may capture image data corresponding to a play area.

As shown in FIGS. 21 to 27 a dealer may place cards on the table withinone or more fields of view of cameras to capture image data relating tocards, dealer card play, and dealer gestures. The image data captured bydifferent cameras with overlapping fields of view may be correlated toimprove gesture recognition, for example.

An example is shown in FIGS. 20 to 31 including illustrative linescorresponding to fields of view for the cameras. As shown in 2000, 2100,2200, and 2300 there may be overlapping fields of view between cameras.FIGS. 24A-24D (shown in images 2400A-2400D illustrate a dealer at atable undertaking motions to serve cards in relation to a card game. Thedealer's motions may temporarily obstruct various betting areas, and itmay be advantageous to have overlapping fields of view to account forsuch obstruction. In some embodiments, where there is a single camera,the shutter speed may be slowed so that the dealer's motions are removedduring processing. Similarly, FIGS. 25A-25E, and FIGS. 26 and 27 at2500A-2500E, 2600 and 2700 show alternate dealing motions.

FIGS. 28-31 illustrate how a computing system may track the variousbetting areas. As provided in the diagrams 2800, 2900, 3000, and 3100, abet recognition system may utilize imaging components and/or sensorsthat process images from the perspective of a dealer. As depicted, threedifferent cameras may be used, each tracking one or more differentbetting areas that are associated with each player. There may bemultiple betting areas that a player may be in (e.g., craps). Asindicated in FIG. 30 and FIG. 31, the fields of view may overlap for thecameras. The overlapping field of view may aid in increasing theconfidence score of a particular bet analysis.

FIG. 32 and FIG. 33 depict camera assembly components 3200 and 3300illustrating an example imaging component in relation to its relativepositioning and orientation. Top view 3200 and bottom view 3300 aredepicted and the camera assembly is designed to be retrofitted onto atray or table at a betting or gaming surface.

As a further example, a bet recognition device may have an imagingcomponent with three cameras at the back of the table (e.g., left,middle, right), pointing inward. An example 3400 is shown in FIG. 34including illustrative lines corresponding to fields of view for thecameras.

As another example, a bet recognition device may have an imagingcomponent with three cameras in the middle of the table (left, middle,right), pointing outward. An example 3500 is shown in FIG. 35 includingillustrative lines corresponding to fields of view for the cameras.

As an additional example, a bet recognition device may have an imagingcomponent with two cameras at the back of the table (left and right),pointing inward. An example 3600 is shown in FIG. 36 includingillustrative lines corresponding to fields of view for the cameras.

As an additional example, a bet recognition device may have an imagingcomponent with seven endoscope cameras placed at the front of the table,between the players and the betting area, pointing inward. Examples 3700and 3900 are shown in FIGS. 37 and 39. Example hardware components3802-3808 for implementing an example imaging component are shown inFIG. 38 at 3800. FIG. 39 illustrates an alternate embodiment 3900wherein cameras are positioned differently.

Endoscopes may be augmented by mirrors and light emitters in someexample embodiments. The use of endoscopes may be an effective methodfor achieving high accuracy when analyzing chips as the cameras arelocated closest to the betting area as possible with no obstructions.However, the effort required to retrofit existing casino tables withendoscopes may be arduous to be practical in some situations.Accordingly, different example camera implementations may be useddepending on the situation.

When capturing imaging data of the betting area for the purposes ofanalyzing the number and value of a player's chips, the cameras maycapture images using infrared (IR) technology which is not visible tothe human eye. The cameras may use IR emitters and receivers which canoperate on the same wavelength. Further, the cameras may be programmedto capture images at different times so that the wavelengths do notinterfere with one another, making sure that each image is notobstructed.

In another aspect, embodiments described herein may provide automaticcalculation of manual casino shoes and associated statistics including,for example, shuffle per hour.

The “casino shoe” is the card release mechanism on casino tables, whichmay contain several decks of cards. Dealers use the casino shoe tosource cards for dealing each hand.

A casino “shoe” may be monitored by hardware components to provide ameasure of how many shuffles occurred per hour, how many cards weredealt to players (including the dealer) per hour, and so on. Forexample, to count cards on a manual shoe, a magnet and a magnetic sensormay be attached to the shoe and used to trigger when the shoe is emptyof cards.

Alternatively, a dealer procedure could require the shoe to be turned onits side with the shoe weighted wedge removed. This can be recognizedwith the retrofitting of the tilt switch (i.e., single-axis gyroscope)inside or outside of the shoe. This may recognize when a shoe has beendepleted and is to be refilled. The bet recognition device 20 can thuscollect data on cards such as, for example, how many shuffles occur perhour (which varies because shoes are depleted at different depths basedon different gameplay scenarios) and indicate when the bet recognitionprocess does not need to look for player bets.

Embodiments described herein automate the process of counting “shoe”related statistics (e.g., the number of shoes). Prior attempts mayrequire data to be collected manually by the pit manager

FIG. 40 illustrates that a shoe 4000 may be positioned at variousinclines (increasing, decreasing). FIGS. 41 to 43 illustrate differentviews 4100, 4200, 4300 of a card shoe which may be used in exampleembodiments. FIGS. 44 to 46C illustrate different example positions4400, 4500, 4600A-4600C of a card shoe on a gaming table according tosome example embodiments.

In another aspect, embodiments described herein may provide backgroundimage subtraction and depth segmentation. Background subtraction is acomputational technique used according to embodiments described hereinto differentiate the background image from the foreground image whencapturing image data of the casino table. Depth segmentation is anothertechnique that may achieve accuracy in analyzing a specific image dataof a three-dimensional area of the betting table. This technique allowsembodiments described herein to establish a region of analysis on thecasino table and to narrow the focus of the camera onto the bettingarea, without incorporating the chips not relating to that player's bet(e.g., in another betting area, or out of play altogether). This areamay be referred to as a “bounding box”.

This is one of different example ways to perform the removal of thebackground of an image when establishing a “bounding box” to identifythe number and types of chips in a betting area. Other exampletechniques include graph cut, frame differencing, mean filtering,Gaussian averaging, background mixture models, and so on.

After capturing two-dimensional images with a camera embedded in thecasino table, background subtraction can be run as an automaticalgorithm that first identifies the boundaries of object, and thenallows the software to separate that object (e.g., chips, hands, or anyother objects commonly found on the casino floor) from its background.

To increase the effectiveness of background subtraction, embodimentsdescribed herein may support three-dimensional background subtraction,making use of depth data to differentiate the foreground image from thebackground (illuminating the object with infrared and/or using lasers toscan the area).

Based on the distortion of the light, after projecting light onto theobject, embodiments described herein can determine how far away anobject is from the camera, which helps to differentiate the object fromits background in two-dimensional and three-dimensional images.

In a further aspect, embodiments described herein may count the numberof stacks of chips placed in the betting area.

Embodiments described herein may be used to administer casino standardsfor how players should place their stacks of chips. Known approachesrequire this to be checked manually by a dealer before a hand can begin.

Embodiments described herein may have the capacity to accurately collectdata on every hand by counting the chips and their value. Embodimentsdescribed herein may have the ability to establish the quality ofbetting by using depth cameras to capture errors made by players.

Embodiments described herein may scan and accurately count any type ofchips, including but not limited to four example varietals: no stripes,all different colors; stripes, but all the same on all chips (differentcolours); varying colors and stripes (stripes are smeared); anddifferent colors and stripes (stripes are well-defined). FIGS. 47 and 48show example views of chip stacks at 4700 and 4800, respectively.

When a camera is elevated above the level of the table, embodimentsdescribed herein may be programmed to detect the top of the chip first.An example 4900 is shown in FIG. 49 at 4902, 4904, and 4906. Once thesystem has recognized the top of the chip, the process can quickly counttop-down to establish an accurate chip count and then shift to scanbottom-up to identify the value of all these chips. This may beimportant due to several example features. Several cameras according toembodiments described herein may be elevated above the tabletop (e.g.,looking past the dealer's body, midway up the torso). This allows thesystem to recognize the betting area by establishing a “bounding box”,which is an area on the betting table that is scanned by to analyzephysical objects, i.e., the chips.

In some casino games, such as baccarat, other chip stacks on the tablemay obstruct players' bets from the camera's view, so the process mayencounter obstruction. To solve this, cameras placed at elevated anglesmay allow embodiments described herein to see more of table and maintainhigh accuracy.

In another aspect, embodiments described herein may determine chip stacksequence in a betting area.

When bets are being placed in betting areas, embodiments describedherein may have the capacity to capture data on the visual sequence ofthe chips when players place them in the betting area.

Embodiments described herein may take pointed pictures (e.g. image data)of varying stripe patterns on a cylinder (e.g. the shape of the chipstack), which can vary in pixel number. In accordance with embodiments,to optimize the speed and accuracy of recognizing chips, the system maybenchmark the size of image captures at 3 by N pixels for height andwidth measurements, in some examples. Other benchmarks may be used.

Embodiments described herein may accurately recognize when a frequencysignature is present and also allows for the training of new stripepatterns, for example.

Embodiments described herein may recognize and confirm this pattern byusing a camera to see the sequence of the stripes, as well as the colorof the chips. The system may be configured to be precise enough toregister the specific hue of the chip, which is relevant for the systemin terms of both accuracy of results and security purposes.

The casino standard of chip stack sequence may typically have playersbet in a specific order: highest value chips on the bottom of the stack,with lower value chips layered upward. FIG. 50 shows a chip stack withthis example standard.

Beyond recognizing the typical pattern (e.g., highest value on bottom,lowest on top (see FIG. 50 for 5000 at 5002-5008) embodiments describedherein may use Fourier analysis to find different types of chip,applying learnable frequency/discrete signal patterns on the chip stack.Embodiments described herein may also combine with Neural Networks orequivalent learning techniques to identify when the pattern is present.By preprogramming the process to recognize particular nodes of theneural network relating to chips, Embodiments described herein can alsodeactivate specific nodes so that they are not optional paths in thedecision process, which may improves both speed and accuracy.

This feature also enables embodiments described herein to “turn off”certain classes or types of chips during its analysis of stack sequence,so that it does not search for particularly values of chip. This has theeffect of increasing the speed of the ‘chip stack sequence’ processwhile maintaining accuracy.

By training multiple classifiers, embodiments described herein canautomatically remove the last highest chip value from each stacksequence to increase the speed of analysis. Each time the algorithmscans the chip stack, it may run the example sequence as follows: 12345(scanning all chips), 1234 (removing a value), 123 (removing anothervalue), 12 (etc.), 1 (etc.), and finally removing all chip classifiersduring its last scan. By removing values over multiple analyses,embodiments described herein may not look for certain values when theyare not present in a player's bet. This may enable the process toincrease its operation speed over time while maintaining accuracy.

Embodiments described herein may also recognize player errors on theoccasions when the stack sequence does not meet the casino standard.

If the player makes an error or a failure to meet the standards of thecasino, embodiments described herein may log that error and may generatean electronic alert for transmission to a casino manager to provide analert of the error. The automatic alert may depend on the severity ofthe error.

Embodiments described herein may also detect between different versionsof a chip using pattern recognition and signal processing techniques.For example, different colors and patterns on chips may have differentfrequencies of image data and which may be used to detect differentversions of chips.

Embodiments described herein may also detect when chips requirecleaning. With the same frequency image techniques used for detectingdifferent versions of the chip, embodiments described herein may alsodetect when dirt has built up on chips. The build-up of debris on thechips disrupts the frequency of its colour as captured by the camera.

Embodiments described herein may also use mirrors on casino tabletops aspart of the image capture component of bet recognition device 20.

Embodiments described herein may require two spaces on the surface topof a casino table to run at its top capacity.

To make these spaces smaller, one approach may be to use mirrors on thetable surface to reflect the camera illumination technology onto thechips, enabling embodiments described herein to effectivelydifferentiate the object from the background to enable the chip countingprocess, and other applications, to run at high capacity. Adding mirrorsto the tabletop can help improve aesthetics, enable easier retrofit, andmake embodiments described herein less obtrusive to the dealer andplayers.

Embodiments described herein may be described in relation to blackjackas an illustrative example. However, embodiments described herein mayanalyze other casino games.

For example, Baccarat is another game at the casino that the presentlydisclosed system can be used to analyze. Baccarat is a comparing cardgame played between two hands on the table, the player's and thebanker's. Each hand of baccarat has three possible outcomes: “player”win, “banker” win, and “tie”.

Embodiments described herein may obtain additional info about the handon the table, in addition to the bet information, such as what cards thedealer and player had in their hands. Embodiments described herein maydetermine what a player bets, and whether the player has won, lost, ortied.

By providing more accurate account of table dynamics, embodimentsdescribed herein may be essential for improving a casinos understandingof the process of how people are playing baccarat.

FIGS. 51 and 52 at 5100 and 5200, respectively, illustrate bettingsurfaces having imaging components with overlapping fields of view.

FIG. 53 is an example workflow 5300, illustrative of some embodiments.FIG. 53 is an example workflow 5300 illustrative of some embodiments.Workflow 5300 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5300 is a process for extracting and saving bet data asobtained from image data. The recognition process 5302 and the featureextraction profess 5304 are provided as examples, illustrative ofexample steps that may be utilized to determine a chip count result.

FIG. 54 is an example workflow 5400 illustrative of some embodiments.Workflow 5400 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5400 is a process for extracting and saving bet data asobtained from image data. At 5402, coordinates are obtained in relationto the bets as represented by chips in the betting areas. For example,coordinates may include chip height, chip width, and a horizon elementthat may be expressed in the form of pixels. The coordinates may beutilized to obtain lists of pixels for sampling from the images,generating a sample space using the list of pixels. Connected componentsare calculated from the sample space, and centroids are calculated foreach of the connected components. At 5404, coordinate information isextracted. Extraction may include creating headers for coordinateinformation feature, mapping sample coordinates into a region ofinterest space into an original color image space, and creating a listof mapped coordinates. At 5406, the system is configured to create anempty ground truth feature, which is a set of data values that arenormalized so that values can be compared against reference featurepoints. At 5408, features are extracted, and at 5410, coordinateinformation, ground truth and customized feature sets are concatenatedand converted to a dataframe at 5412. The customized feature sets arederived, for example, in relation to distinguishing features of chipmarkings that may vary between different facilities and the types ofchips being utilized.

FIG. 55 is an example workflow 5500 illustrative of some embodiments.Workflow 5500 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible. 5500 is a process for counting bet data as obtained from imagedata. At 5502, images are loaded from a data storage. During the imageloading step, color images may be obtained, and in some embodiments,depth and/or infrared imaging is obtained. A transformation matrix iscalculated, and the color image is read. At 5504, labels are loaded ontothe images (labels are originally blank, and will be updated when theregions of interest in the images are classified), and at 5506, featuresare extracted from the loaded images and labels. The feature extractionprocess, for example, may utilize a trained classifier wherein randomcolors may be assigned to classes to distinguish between differentclasses. During the feature extraction process, one or more lines (e.g.,vertical, horizontal, diagonal) are used for sampling pixels and/or dotrepresentations of the chips.

In some embodiments, the chip pixels themselves in a region of interestrepresenting a particular chip are blurred and/or otherwise aggregatedso that the sampled regions are more likely representative of the chipin its entirety. Such an approach may reduce the number of pixelsrequired for analysis and/or storage, increasing the speed andefficiency of classification. The dots and the pixels may thereforerepresent adjacent colours, and based on the height and distance, thenumber of chips can be determined, and each chip can be segregated bydividing the height of a stack by the height of a chip, creatingindividual chip segments. In some embodiments, the sample line is a 1Dline of color/histogram values, and in other embodiments, a long 2D linehaving a length and a width of values are extracted.

This approach may be helpful where gaming facilities release differentversions of chips, often differentiated by subtle differences in hue(e.g., changing the frequency of color as captured by the imagingcomponent), or in the sequence of markings, such as stripes.

Accordingly, in some embodiments, classification include normalizing thepixel values of an image capture, gamma-decoding the image such thatpixel values are proportional to the number of photos impacting thecamera sensor, combining the pixels in the height dimension into asingle 1D line, which is then truncated to form a uniform width for aFast Fourier Transform analysis. Such an approach may also includeclassification based, for example, at least on the magnitude of thecomplex sinusoids returned.

Colors, among other visual markers, may be mapped to various classes. At5508, chips are classified (e.g., based on machine-vision derivedestimations). The system may also be trained to differentiate betweennew versions of chips from obsolete versions of chips, which may beremoved from circulation to maintain security and consistency.

At 5510, chips are counted through based on the classifications, and at5512, chip counts are printed to a file (e.g. encapsulated and/orencoded for transmission to downstream systems).

FIG. 56 is an example workflow 5600 illustrative of some embodiments.Workflow 5600 includes various steps, and the steps provided areexamples, and different, alternate, less, more steps may be included.While steps may be performed in the order depicted, other orders may bepossible.

At 5602, detecting, by an imaging component, that one or more chips havebeen placed in one or more defined bet areas on a gaming surface, eachchip of the one or more chips having one or more visual identifiersrepresentative of a face value associated with the chip, the one or morechips arranged in one or more stacks of chips.

At 5604, capturing, by the imaging component, image data correspondingto the one or more chips positioned on the gaming surface, the capturingtriggered by the detection that the one or more chips have been placedin the one or more defined bet areas.

At 5606, transforming, by an image processing engine, the image data togenerate a subset of the image data relating to the one or more stacksof chips, the subset of image data isolating images of the one or morestacks from the image data.

At 5608, recognizing, by an image recognizer engine, the one or morechips composing the one or more stacks, the recognizer engine generatingand associating metadata representative of (i) a timestamp correspondingto when the image data was obtained, (ii) one or more estimated positionvalues associated with the one or more chips, and (iii) one or more facevalues associated with the one or more chips based on the presence ofthe one or more visual identifiers.

At 5610 segmenting, by the image recognizer engine, the subset of imagedata and with the metadata representative of the one or more estimatedposition values with the one or more chips to generate one or moreprocessed image segments, each processed image segment corresponding toa chip of the one or more chips and including metadata indicative of anestimated face value and position.

At 5612, determining, by a game monitoring engine, one or more bet datavalues, each bet data value corresponding to a bet area of the one ormore defined bet areas, and determined using at least the number ofchips visible in each of the one or more bet areas extracted from theprocessed image segments and the metadata indicative of the face valueof the one or more chips.

The advantages of the some embodiments are further illustrated by thefollowing examples. The examples and their particular details set forthherein are presented for illustration only and should not be construedas limitations.

In implementation, the process of patching together images may beginwith capturing a particular number of samples from each camera that ismounted to the table.

Different scenarios of chips are used for each sample. These scenariosalso include extreme situations so that the machine can learn, whichallows it to handle simpler scenarios with a greater relative ease. Thecaptured samples are then labeled by denomination to create the filethat is used in training.

The capturing tools developed by the Applicant have been capable offocusing mainly on the bet area, while omitting any surroundingenvironments that might cause discrepancies. The removal of surroundingenvironments helps the system to ignore any background chips duringtraining and testing processes.

During testing, it was noted that the removal of background chipsimproved accuracy. In the process of training and testing, higheraccuracy of datasets and successful training were found throughcapturing and labelling samples in a brightly lit setting and testingthem in a dimly lit setting, or executing both processes in a brightlylit setting. This approach was found to produce a higher accuracy thanperforming both of the process in a dimly lit setting or performing thefirst process in a dimly lit setting while next in bright light.

Applicant also found that providing more light from the side helped thesystem identify colors better.

The embodiments of the devices, systems, and methods described hereinmay be implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references will be maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices. It should be appreciated that theuse of such terms is deemed to represent one or more computing deviceshaving at least one processor configured to execute softwareinstructions stored on a computer readable tangible, non-transitorymedium. For example, a server can include one or more computersoperating as a web server, database server, or other type of computerserver in a manner to fulfill described roles, responsibilities, orfunctions.

The discussion provides many example embodiments. Although eachembodiment represents a single combination of inventive elements, otherexamples may include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, other remainingcombinations of A, B, C, or D, may also be used.

The term “connected” or “coupled to” may include both direct coupling(in which two elements that are coupled to each other contact eachother) and indirect coupling (in which at least one additional elementis located between the two elements).

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be a compact disk read-onlymemory (CD-ROM), a USB flash disk, or a removable hard disk. Thesoftware product includes a number of instructions that enable acomputer device (personal computer, server, or network device) toexecute the methods provided by the embodiments.

The embodiments described herein are implemented by physical computerhardware, including computing devices, servers, receivers, transmitters,processors, memory, displays, and networks. The embodiments describedherein provide useful physical machines and particularly configuredcomputer hardware arrangements. The embodiments described herein aredirected to electronic machines and methods implemented by electronicmachines adapted for processing and transforming electromagnetic signalswhich represent various types of information. The embodiments describedherein pervasively and integrally relate to machines, and their uses;and the embodiments described herein have no meaning or practicalapplicability outside their use with computer hardware, machines, andvarious hardware components. Substituting the physical hardwareparticularly configured to implement various acts for non-physicalhardware, using mental steps for example, may substantially affect theway the embodiments work. Such computer hardware limitations are clearlyessential elements of the embodiments described herein, and they cannotbe omitted or substituted for mental means without having a materialeffect on the operation and structure of the embodiments describedherein. The computer hardware is essential to implement the variousembodiments described herein and is not merely used to perform stepsexpeditiously and in an efficient manner.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein.

Moreover, the scope of some embodiments is not intended to be limited tothe particular embodiments of the process, machine, manufacture,composition of matter, means, methods and steps described in thespecification. As one of ordinary skill in the art will readilyappreciate from some embodiments, processes, machines, manufacture,compositions of matter, means, methods, or steps, presently existing orlater to be developed, that perform substantially the same function orachieve substantially the same result as the corresponding embodimentsdescribed herein may be utilized. Accordingly, embodiments are intendedto include within their scope such processes, machines, manufacture,compositions of matter, means, methods, or steps.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

FIG. 57 is a flow diagram of an example process for bet recognitionsystem 100 according to some embodiments.

At 5710, imaging component 202 captures image data. The image data cancorrespond to the one or more chips positioned in at least one bettingarea on a gaming surface of the respective gaming table. Each clienthardware device can comprise one or more sensors responsive toactivation events and deactivation events to trigger capture of theimage data by the imaging component. The imaging component can bepositioned to capture images of a gaming surface of the respectivegaming table. For example, the images acquired can correspond todifferent channels. In some embodiments, a separate imaging component202 can each capture one of colour (e.g. RGB that captures R, G, and Bchannels), depth (e.g., multiple imaging components that enablecapturing of image data that can be compared and processed to generatedepth data), and infrared (IR) data. For example, an imaging component202 capturing RGB data can generate one or more images of a 1920×1080pixel size, an imaging component capturing depth data can generate oneor more images of a 640×480 pixel size, and imaging component capturingIR data can generate one or more images of a 640×480 pixel size.

In some embodiments, bet recognition system 200 or components thereofcaptures images across a plurality of channels including at least a redchannel, a green channel, a blue channel, a depth information channel,and an infrared channel. Histograms are generated wherein eachrepresentative histogram is an aggregated histogram generated fromcombining histograms generated for each channel of the plurality of thechannels.

At 5720, a processor 204 is configured to extract aggregated frames orkey frames from image data captured by each imaging component 202, forexample, image data across each of the RGB channels. The key frames aregenerated by averaging or calibrating a number of frames of image datafrom one or more of RGB channels, depth image data, and IR image data.Calibration can be enabled by time and/or position synchronization ofthe image data, using known location of the imaging components used tocapture the data and/or detection of similar images captured bydifferent imaging components. In some embodiments, captured image datais represented by an aggregated frame corresponding to average imagedata of image data captured across a duration of time to reducetransient effects arising from temporary visual obstructions of theimaging component.

In some embodiments, a key frame can be generated for each channel. Forexample, forty frames can be captured in colour (e.g., RGB) by animaging component 202, forty frames can be captured over an IR channel,and forty frames can be captured over a depth channel. Each framecaptured can have a corresponding frame in each of the other channels. Aprocessor 204 is configured to select frames from each of the channelscaptured or generated, for example, can select colour, IR, and depthframes based on presence of obstructions of a region of interest. Theremaining frames over the different channels can be averaged to generatea key frame image, thereby removing one or more obstructions captured inthe set of 40 frames.

In some embodiments, one or more cameras can be used, for example, oneor more IR cameras, with each camera capturing one or more frames ofimages.

Generation and use of a key frame can minimize transmission costs ofimages or data for processing, for example, as compared to transmissionor processing of many frames from many channels of an image. This canallow for practically useful classification of chip type andquantification of chip or betting object values in a region of interestor betting location. For example, classification of betting objects andquantification of betting object types can be performed by system 200with timing appropriate to allow for appropriate response to theclassification or quantification. An administrator can be alerted bysystem 200 that a game participant has removed chips from their chipstack at a time that is prohibited by the game and respond to correctthe game participant. The alert can be generated by system 200 based onthe classification or quantification immediately after the chip stackhas been altered. System 200 can immediately trigger the alert after thechip stack has been altered due to the processing time having beenminimized by its use of key frames.

Processor 204 also can be configured to calibrate image data of the samegaming objects to generate depth data. Processor 204 may also beconfigured to use other image data, for example, IR data, in combinationto generate depth data, decide how far away an object is, or decide onan object's location. For example, processor 204 can be configured todetermine distance of an object based on how light or a light patternwarps over an object as captured by an IR imaging component. Thisdistance information can be compared with other distance information ofthe same object captured by a separate imaging component located a knowndistance away. In some embodiments, two RGB cameras can be used togenerate depth information using triangulation.

In some embodiments, a specified number of frames of image data can beselected and averaged to generate a key frame that can be used forfurther processing. This can allow transient obstructions of an objector region of interest to be removed or removed to an acceptable extent.In some embodiments, captured image data is represented by an aggregatedframe corresponding to average image data of image data captured acrossa duration of time to reduce transient effects arising from temporaryvisual obstructions of the imaging component. This process of generatinga key frame from averaged images can be repeated across each channelcaptured or across different colour spaces (e.g., RGB and Y′CbCr). Insome embodiments, a single key frame is generated from averaged framescaptured over multiple different channels after frames containingobstructions are removed. At 5730, a processor 204 is configured topre-process the captured image to filter at least a portion ofbackground image data to generate a compressed set of image data of theone or more chips free of the background image data. Here, by exploitingthe fact that the depth values representing the chips should varysmoothly in a specific bet area, processor 204 employs a histogram ofdepth to extract and localise chips in a particular bet area from itssurroundings. Background objects such as people, hands, cards, otherchip stacks, and other objects can be filtered out as background imagedata.

In some embodiments, processor 204 can define a regions of interest(e.g., a betting location) based on threshold values such as thresholddepth data. Processor 204 can be configured to use the defined regionsof interest to identify a foreground object and/or separate backgroundfrom foreground image data. For example, processor 204 can be configuredto separate foreground objects, including chip stacks, from backgroundobjects by identifying image data (e.g., key frame image data)corresponding to objects within a region of interest, such as a bettinglocation (e.g., where a region of interest is defined by thresholdvalues of depth data). Data corresponding to background objects can bediscarded, in some embodiments. At 5731, processor 204 is configured toextract regions of interest, for example, corresponding to a bet volumesuch as a chip, stack of chips, or two or more adjacent chips within astack of chips, based on depth information allowing identification ofchips.

At 5732, processor 204 is configured to generate rotation invariantimage data for each region of interest and remove noise, for example,image noise or data corresponding to obstructions covering a part of thebet volume. For example, background regions of an image can be removedas noise. In some embodiments, processor 204 is configured to use medianfiltering to remove noise from images.

In some embodiments, where there is no noise, no noise removal step isperformed. In some embodiments, processor 204 is configured to generaterotation invariant image data and subsequently generate scale invariantimage data immediately after, without a noise removal step. Notperforming noise removal can improve the accuracy with which images arerepresented, for example, as data. In some embodiments, images processedby processor 204 do not have salt and pepper noise, so noise removal onthese images, for example, by median filtering, would decrease theaccuracy of the images (e.g., by producing artifacts) rather thanimprove its accuracy or representation. Noise removal can be omitted asa redundant step in these embodiments.

At 5733, processor 204 is configured to generate rotation and scaleinvariant image data, for example, for each region of interest or betvolume. This allows the size or scale of the regions of interest (e.g.,chip stack) or portions thereof to be represented in accurate scale whenresized or rotated. Processor 204 also can be configured to normalizethe image data for each region of interest. At 5734, processor 204 canbe configured to extend channels by transforming the image, for example,transforming the image from RGB to a different color space whereintensity is decoupled from color information or luminance is decoupledfrom chrominance. Examples of such color spaces include L*a*b*, HSV, andHSL. For example, processor 204 can be configured to generate YCbCrimage data based on image data captured from red, green, and bluechannels. The YCbCr colour space can be used because in the YCbCr colourspace, luminance is decoupled from chrominance, i.e., intensity isdecoupled from color. This can improve the subsequent processing,transmission, and storage features and quality of the image data. Forexample, the luminance and chrominance can be processed separately. Thiscan allow for improved compression of the data (e.g., for reducedbandwidth consumption during transmission) while enabling a satisfactoryrepresentation of the image from the compressed data.

In some embodiments, processor 204 is not configured to perform depthnormalization after generating scale invariant image data. Depthnormalization can be omitted after the scale invariance step, forexample, due to chip localization being performed. During chiplocalization, processor 204 is configured to identify foreground objects(e.g., chips in a region of interest or betting location) and assignwhatever is in the background to 0, thereby removing background imagedata. In embodiments where depth normalization is performed, the normaldepth of the foreground chips are used to normalize the depth image inorder to give less weight to the background chips and give higher weightto the foreground chips. However, in embodiments where chip localizationis performed, since the background and the foreground are isolatedduring chip localization, depth normalization can be removed in someembodiments. Depth information is used by processor 204 to identify theforeground objects (e.g., chip stacks, chips, etc.), for example, todetermine whether anything is placed within a bet spot or region ofinterest.

At 5735, 5736, and 5737, processor 204 is configured to extract andgenerate data to produce a horizontal gradient of the red, green, andblue channels, respectively.

At 5740, processor 204 is configured to perform feature extraction toidentify points of interest on betting objects in a bet volume such as achip or stack of chips. This occurs according to the steps described at5742, 5744, 5746, and 5748.

At 5742, processor 204 is configured to identify pre-processed imagedata corresponding to one or more betting objects. This data can beselected from a set of data corresponding to regions of interest.Processor 204 is configured to identify regions of interest based onthreshold values of depth data. In some embodiments, processor 204 canbe configured to execute instructions in memory to configure a trainedclassifier to identify regions of interest or objects within regions ofinterest, such as image data corresponding to one or more bet volumes,chip stacks, or betting objects.

At 5744, processor 204 is configured to select and extract data encodingpoints of interest within regions of interest. These points of interestscan be used as features in a classifier to predict the type of a bettingobject, such as a chip type. This can enable the classifier to betrained to more accurately predict chip types using only the selectedfeatures, avoiding overfitting of the classifier to too many variablesin image data. Overfitting could otherwise reduce accuracy of theclassifier in classifying chip type of image datasets. The classifierwould be optimized to classify only the training data, with limitedgeneralizability to new data and, in turn, limited usefulness inproviding chip type classification. Therefore, selection of a limitednumber of data variables can improve generalizability of the classifierand its ability to provide accurate classification of chip type from newdata. The data variables can be selected as those features that canimpact parameters of a random forest classifier, for example, tree depthand how nodes are split.

For example, the points of interest can be selected so points ofinterest are arranged in a series of straight rows spanning the width ofa region of interest, that is, image data corresponding to data capturedof a location on an object located at the same y-axis value. In someembodiments, points of interest can be selected by a pre-defined overlaygenerated by processor 204. As a separate way, as in some embodiments,points of interest for the region of interest can be selected using thedata representing the leftmost edge of the region of interest as areference point. For example, the first point of interest can beselected at a first defined interval away from the leftmost edge (e.g.,to avoid selecting image data not representative of the region ofinterest due to image effects from proximity to other objects) andsubsequent points of interest at the same y-axis value can be selected asecond defined interval away from the previously selected point ofinterest. The scale invariant data can enable identification of theabsolute location of each object.

In some embodiments, after chips have been localized (e.g., by removingbackground image data), points of interest are selected within a regionof interest. Processor 204 is configured to select points of interestusing equal spacing in a horizontal direction within the region ofinterest. Additional points of interest can be selected using equalspacing in a vertical direction within the region of interest. In someembodiments, the respective equal spacings can be different inhorizontal and vertical directions.

At 5746, processor 204 is configured to generate a histogram at eachpoint of interest corresponding to data captured by one or more imagingcomponents or data generated from same. In some embodiments, processor204 is configured to generate new, extended channels from data inchannels directly captured by one or more imaging components that eachcapture, for example, one of RGB, IR, and depth data. Processor 204 isconfigured to extend the plurality of channels by transforming thecaptured image data from RGB to a different colour space where intensityis decoupled from color information or luminance is decoupled fromchrominance, the transformations yielding additional channels in theplurality of channels. This data corresponding to each point of interestcan be encoded as a histogram, with a histogram generated for eachchannel for each point of interest.

Processor 204 is configured to generate a histogram of gradients foreach point of interest based on a horizontal gradients generated fromimage data of a region of interest. A 3×3 Sobel operator is applied toone or more channels (e.g., the R, G, and B channels and/or otherchannels) of image data of the region of interest to generate agradient. In some embodiments, other operators can be used to generategradient data. Processor 204 then generates a histogram of the gradientsfor each point of interest or region of interest. This may help minimizenoise in the representation or storage of the image data or portions ofthe image data.

At 5748, processor 204 is configured to concatenate or otherwiseassociate, combine, average, or extract data from each histogram for asingle point of interest. In this way, processor 204 is configured togenerate a single histogram of all channels against each point ofinterest. The single histogram for a point of interest can plot a vectoror array where each position in the vector or array encodes the totalmagnitude of all gradients of all channels for a single gradientdirection. This concatenation or association of all histogram data foreach point of interest can provide a more robust dataset forclassification of chip type by a trained classifier (or, in the contextof the generation of a trained classifier, can provide a more robustdataset for feature selection and training of a classifier).

For example, in an embodiment, for each channel captured by an imagingcomponent 202 or generated, processor 204 is configured to generate ahorizontal gradient. There can be a stack of eleven channels for asingle image, with five channels captured by a camera and six additionalchannels generated by processor 204. At each point of interest (e.g., atx coordinate 10, y coordinate 10), a small selection of image data ismade in corresponding locations across each of the channels. The smallselection of image data can be the image data encoding the portions ofthe image immediately adjacent to the point of interest. Processor 204is configured to generate a histogram for each point of interest basedon each respective small selection of image data. Each histogramgenerated from each channel for a point of interest is concatenated.That is, in some embodiments, a histogram of horizontal gradients isgenerated for each channel for each point of interest and each histogramfor a given point of interest is concatenated and can be used forfeature extraction.

In some embodiments, the small selection of image data for a point ofinterest is the data encoding the portion of an image, where the portionof the image is that portion that lies within a rectangular window andincludes the respective point of interest. An example window for a pointof interest is depicted at the left portion of FIG. 75. Processor 204 isconfigured to generate a concatenated histogram based on image data fromone or more or all channels encoding the portion of the image fallingwithin the window. Processor 204 is configured to generate aconcatenated histogram corresponding to each point of interest. Thewindow size for a point of interest can be selected to cover, forexample, approximately 80% of the length of a chip in a chip stack andapproximately the entire height of a chip.

In some embodiments, different shapes (e.g., rectangular, circular,etc.) can be used for the window used to calculate the histograms. Arectangular window shape may correspond more closely to a chip in animage captured by imaging component 202. In this way, processor 204 canbe configured to use this image data representing the portion of theimage falling within the window to generate a classification (e.g., ofchip colour) for each point of interest.

At 5750, processor 204 is configured to process the compressed set ofimage data to establish a two-dimensional grid comprising points ofinterest overlaid over the compressed set of image data, and for eachpoint of interest, classifies the point of interest (e.g., as colour, orbackground) based on an analysis of a corresponding representativehistogram generated based on the image data corresponding to thecorresponding point of interest. For example, each histogram can plot avector or array, where each position in the vector or array encodes thetotal magnitude of all gradients for a single gradient direction. Ahistogram can be generated for portions of image data, for example,where each portion corresponds to a single point of interest. That is,each point of interest can be represented as gradient values encodingthe magnitude and direction of each pixel in the selected point ofinterest. These gradient values for the point of interest can be used togenerate a vector storing total magnitudes for select gradientdirections spanning the range of possible gradient directions (e.g., 0to 160, where vector location 0 represents magnitudes with 0 or 180degree directionality). The vector can be used to generate a histogramdepicting the magnitude of gradients for all horizontal gradients at theportion of the image representing the point of interest.

A histogram of gradients (e.g., HoG) can be used in some embodiments.

In some embodiments, the horizontal gradients can be calculated by usinga 3×3 Sobel operator on one or more channels, for example, the R, G, andB channels and/or channels in other colour spaces, and correspondinghorizontal gradient channels in order to capture the vertical texture inthe bet objects, bet volume, or chips. This can enable encoding of chiptexture, for example, for classification of chip type or colour at eachpoint of interest and of each chip. This can also enable edge detectionof betting objects or chips, for example, based on verticaldirectionality encoded in a generated vector representation of the imagegradient data. In some embodiments, processor 204 is configured tonormalize each histogram, image data, and/or vector to reduce impact oflighting variations.

The processor 204 is configured to identify a dominant classification(e.g., “yellow” or “striped blue and gray”) for each row in a grid ofthe points of interest. In some cases, the dominant classification canbe determined by majority voting or selection of the most frequentlyclassified type amongst the classified types for each point of interestin the row. The classification for each point of interest can be basedon features selected from a concatenated histogram of horizontalgradients, where the concatenated histogram is generated using imagedata encoding a rectangular window spanning a large portion of the widthof a row of points of interest (e.g., the width of a chip in a chipstack). Using a large window can allow striped chips to be assigned adifferent dominant classification than chips wholly coloured one of thestripe colours. This can allow the system to accurately identify andquantify chips and bet volumes.

In some embodiments, histograms generated for multiple points ofinterest within the same row or at the same height are used to togetheridentify a chip, for example, by majority voting. The multiple points ofinterest are selected as points within a rectangular window spanning thechip.

The dominant classification is recorded in a data structure to establisha vector representation (e.g., a single dimensional array) of the one ormore chips in the at least one betting area.

In some embodiments, the dominant classification can be determined by aclassifier trained on sets of classification data along points ofinterest along an x-axis. This classifier can be trained to distinguishbetween blue chips, gray chips, and chips striped in an evendistribution of blue and gray.

For example, in some embodiments, processor 204 is configured togenerate a classifier using one or more classification models, based on,for example, random forest or neural network algorithms. Processor 204is configured to cause the classifier to be trained on image data, forexample, to predict a point of interest type (e.g., colour, background,etc.) based on histogram data, for example encoding a concatenatedhistogram generated from multiple channels at a single point ofinterest. Depending on the classification model used, this can involveparameter selection or identification of the best features forclassification. The classifier can be tested or validated iteratively ordynamically using new image data captured as betting objects are usedduring betting events in a live game. After the classifier is trained,the classifier is then used to classify each point of interest overlaidover a compressed set of image data, for example, points of interest fora selected region of interest (e.g., bet volume or stack of chips). Theclassification for each point of interest is based on the histogramgenerated for the point of interest and based on image data frommultiple channels captured. The classification for each region ofinterest can denote a colour, whether it is background or a bettingobject, etc. The trained classifier can be generated at an earlier timethan its real-time use to classify betting objects during bettingevents.

At 5760, processor 204 is configured to determine one or more quantitiesof one or more chip types of the one or more chips in the at least onebetting area by processing the vector representation based at least on acomparison with physical geometric characteristics of the one or morechip types. For example, a physical geometric characteristic can includea centroid determined for each bet volume and the number of chips ineach bet volume determined based on the values stored in the vectorrepresentation and the nature of those values in relation to adjacentvalues in the vector representation.

For example, at 5762, processor 204 is configured to find the centroidof a stack of chips for each bet volume. This can be the midpoint alongan x-axis of a chip, for example, the length defined by the median ofthe chips' corresponding points of interest projected along an x-axis.In some embodiments, processor 204 selects or transform the data tocompensate for the angle that the images are captured at.

In some embodiments, the height and centroid of a stack of chips orsingle chip or bet object can be calculated using chip localization(e.g., isolation of foreground image data from background image datausing depth data of objects in a region of interest or betting location,where each region of interest or betting location is defined bythreshold depth data values). Using a dominant classification for eachrow and the height and/or centroid data, processor 204 is configured toquantify the chips in the chip stack, including determining chip type,quantity of each chip type in a chip stack, and total value of one ormore chip stacks, bet volumes, or bet objects. Processor 204 isconfigured, at 5764, to find chip types in each bet volume and, at 5766,calculate a number of chips in each bet volume.

Processor 204 can be configured to cause a data storage to maintain adata structure storing one or more data fields representative of thedetermined one or more quantities of one or more chip types of the oneor more chips in the at least one betting area. The data structure canuse one or more of various implementations, such as, a relationaldatabase, non-relational database, flat file, etc. This information canbe displayed in an interface element in real-time to a dealer at thebetting table or transmitted to a server for trend analysis, forexample.

In an example embodiment, a process for monitoring game activities at agaming table comprises: capturing image data corresponding to the one ormore chips positioned in at least one betting area on a gaming surfaceof the respective gaming table; pre-processing the captured image datato filter at least a portion of background image data to generate acompressed set of image data of the one or more chips free of thebackground image data;

processing the compressed set of image data to establish atwo-dimensional grid comprising points of interest overlaid over thecompressed set of image data, and for each point of interest, classifythe point of interest based on an analysis of a correspondingrepresentative histogram generated based on the image data correspondingto the corresponding point of interest; identifying a dominantclassification for each row in the grid of the points of interest, thedominant classification recorded in a data structure to establish avector representation of the one or more chips in the at least onebetting area; determining one or more quantities of one or more chiptypes of the one or more chips in the at least one betting area byprocessing the vector representation based at least a comparison withphysical geometric characteristics of the one or more chip types; andmaintaining a data structure storing one or more data fieldsrepresentative of the determined one or more quantities of one or morechip types of the one or more chips in the at least one betting area.

In an example embodiment, the process further comprises pre-processingthe captured image data to apply at least one of rotation and scaleinvariance.

In an example embodiment, there is provided a device for monitoring gameactivities at a gaming table comprising: an imaging component positionedon a gaming table or proximate thereto to capture image datacorresponding to the one or more chips positioned in at least onebetting area on a gaming surface of the respective gaming table, eachimaging component comprising one or more sensors responsive toactivation events and deactivation events to trigger capture of theimage data by the imaging component, the imaging component positioned tocapture images of a gaming surface of the respective gaming table; aprocessor configured to pre-process the captured image data to filter atleast a portion of background image data to generate a compressed set ofimage data of the one or more chips free of the background image data;the processor further configured to process the compressed set of imagedata to establish a two-dimensional grid comprising points of interestoverlaid over the compressed set of image data, and for each point ofinterest, classify the point of interest based on an analysis of acorresponding representative histogram generated based on the image datacorresponding to the corresponding point of interest; the processorfurther configured to identify a dominant classification for each row inthe grid of the points of interest, the dominant classification recordedin a data structure to establish a vector representation of the one ormore chips in the at least one betting area; the processor furtherconfigured to determine one or more quantities of one or more chip typesof the one or more chips in the at least one betting area by processingthe vector representation based at least a comparison with physicalgeometric characteristics of the one or more chip types; and a datastorage configured to maintain a data structure storing one or more datafields representative of the determined one or more quantities of one ormore chip types of the one or more chips in the at least one bettingarea.

A device for monitoring game activities at a gaming table comprising: animaging component positioned on a gaming table or proximate thereto tocapture image data corresponding to the one or more chips positioned inat least one betting area on a gaming surface of the respective gamingtable, each imaging component positioned to capture images of a gamingsurface of the respective gaming table; a processor configured topre-process the captured image data to filter at least a portion ofbackground image data to generate a compressed set of image data of theone or more chips free of the background image data; the processorfurther configured to process the compressed set of image data toestablish a two-dimensional grid comprising points of interest overlaidover the compressed set of image data, and for each point of interest,classify the point of interest based on an analysis of a correspondingrepresentative histogram generated based on the image data correspondingto the corresponding point of interest; the processor further configuredto identify a dominant classification for each row in the grid of thepoints of interest, the dominant classification recorded in a datastructure to establish a vector representation of the one or more chipsin the at least one betting area; the processor further configured todetermine one or more quantities of one or more chip types of the one ormore chips in the at least one betting area by processing the vectorrepresentation based at least a comparison with physical geometriccharacteristics of the one or more chip types; and a data storageconfigured to maintain a data structure storing one or more data fieldsrepresentative of the determined one or more quantities of one or morechip types of the one or more chips in the at least one betting area.

In some embodiments, the captured image data is captured across aplurality of channels including at least a red channel, a green channel,a blue channel, a depth information channel, and an infrared channel;and wherein each representative histogram is an aggregated histogramgenerated from combining histograms generated for each channel of theplurality of the channels. In some embodiments, horizontal gradients arecalculated using a 3×3 Sobel operator in order to capture the verticaltexture in the chips.

FIG. 58 is a view of an example game monitoring system 1008, includingbet recognition system 200, according to some embodiments. Imagingcomponent 202 can capture images in various channels, including RGB andIR and can also capture images allowing generation of depth information.One or more imaging components 202 can be retrofitted in a gamingestablishment without modification to existing betting tables orexisting betting objects. The imaging components 202 can be installed inan arrangement on a betting table that allows optimal image capturingfor classification of betting object type (e.g., chip type), includingdistinguishing between relevant betting objects and other objects (e.g.,background) using depth information. In some embodiments, imagingcomponent 202 can be positioned at an angle or elevation away from abetting surface or gaming table. For example, one or more imagingcomponents 202 or bet recognition system 200 can be positioned overheadabove the table or an imaging component 202 can be positioned at eachcorner of a gaming table or betting surface. The imaging components 202can communicate with gaming table or bet recognition system 200 (orcomponents of same) wirelessly or via wired connections. In someembodiments, one or more imaging components 202 or bet recognitionsystem 200 can be retrofitted on a gaming table or betting surface andone or more other imaging components 202 can be positioned at an angle,elevation, or distance away from the gaming table or betting surface.The fields of view that can be captured by the one or more imagingcomponents 202 can be synchronized using time stamps and/or imagerecognition of same or similar objects. Different image channels,angles, perspectives, or data captured from the same or similar objectscan be associated with each other. This can provide enhanced data forbet object recognition, generation of depth data, feature extraction,filtering, channel generation, key frame extraction, and/or removal oftransient obstructions of the bet object.

Bet recognition system 200 or components thereof can include acommunication link, for example, a wired, wireless, synchronous,asynchronous, or bulk link, configured for transmitting data (e.g., datastructure or a subset of the data structure) to generate betting datafor the gaming table. The betting data can include betting amounts forthe at least one betting area. Bet recognition system 200 or componentsthereof can transmit the data to external systems or databases 252 or toother devices, for example, over a network 270.

In some embodiments, point of interest extractor 222 extracts points ofinterests from the image data (or processed image data) received fromimaging component 202 or from image processing engine 204. These pointsof interest may span “bounding boxes” around betting objects (e.g., chipstacks or bet volumes) that include the pixels used for analysis. Pointof interest extractor 222 transmits the image data for each point ofinterest (e.g., data from each of multiple channels capturing data atthe point of interest) and any associated data (e.g., meta data, datarelating a point of interest to its location) to histogram generator224.

Histogram generator 224 can generate a histogram for data from eachchannel for each point of interest. Histogram generator 224 can generatea dominant histogram for each point of interest using the multiplehistograms associated with the various channels for a single point ofinterest. Histogram generator 224 can receive data from image processingengine 204 and/or point of interest extractor 222 to generate a singlehistogram for each point of interest.

Histogram generator 224 can transmit the histogram for each point ofinterest to a classifier, for example, a random forest classifier. Insome embodiments, training data can be provided to the classifier 226 togenerate a trained classifier or to re-train a classifier to optimize orfine-tune its classification. Trained random forest classifier 226 canclassify the histogram data for each point of interest to determine abetting object type (e.g., chip type).

In some embodiments, a dominant classification for a set of points ofinterest (for example, a horizontal row) can be generated by majorityvoting. In some embodiments, trained classifier 226 can classify the setof classifications of points of interest within a certain location(e.g., those points of interest along the same y-value) to determine adominant classification of a chip. In some embodiments, image processingrecognizer engine 206 can instead identify a dominant classification byother means, for example, by selecting the most common classification ofthe points of interest.

Image recognizer engine 206 can apply recognition techniques todetermine betting object type (e.g., actual chip value) for each bettingobject (e.g., chip) in a relevant region of interest represented by aset of points of interest. For example, image recognizer engine 206 cancommunicate with or utilize trained classifier 226 to identify a set ofpoints of interest corresponding to a single chip. This information canbe used to determine each chip type (and in turn, quantify the chips ofa given type in a chip stack or bet volume) once the dominantclassification of the points of interest corresponding to each chip isdetermined.

FIG. 59 is a diagram of an example playing surface with betting areasand fields of view of imaging components 202. Each fields of view 5910a, 5910 b, 5910 c, and 5910 d can cover an area of the playing surfaceincluding betting areas. Each area covered by a single field of view mayoverlap with an area covered by a separate field of view. Overlappingportions of fields of view can be used by game monitoring system 100 bto generate multiple and/or different channels or imaging data of thesame betting object (e.g., chip stack). For example, perspective viewsof a chip stack can be used to generate depth data by an imagingcomponent 202 or image processing engine 204.

FIG. 60 is a diagram of an example playing surface with betting areasand fields of view of imaging components 202. One or more imagingcomponents 202 can produce image data for RGB channels 6010, depth 6020,and IR channels 6030 for a single field of view. Game monitoring system100 b can generate additional images from the channels captured. Forexample, the respective images generated from RGB channels, depth data,and IR channels are depicted at 6010, 6020, and 6030, respectively.

FIG. 61 is a diagram of an example set of image data including image6110 representing capture across RGB channels, image 6120 representingdifferential depth information, and image 6130 representing captureacross an IR channel. An imaging component 202 can capture image dataand generate representations 6110, 6120, and 6130.

At 6120, a first image of a chip stack is shown as well as a secondimage depicting a horizontal gradient of R, G, and B channels of thefirst image. Gradient data can be used by processor 204 for featureextraction and classification of bet object or chip type.

FIG. 62 is a diagram of an example representation of image data showingdepth on a green channel. Image processing engine 204 can combine imagedata or representations of same into a single representation or display.This may involve calibration and synchronization across two or moreimages (or data representing same) to allow depiction of differentialimage capture over the same objects from the same perspective. Forexample, image processing engine 204 can receive first image dataallowing representations of data capture across the green channel froman RGB camera within imaging component 202. Image processing engine 204can also receive second image data allowing representations of datacapture from cameras within imaging component 202 allowing presentationof depth information. Image processing engine 204 can overlay the firstand second image data and adjust the representation (for example, usingdata about the location of capture corresponding to each data point) sothat the overlays line up according to the locations corresponding tothe data presented, for example, are synchronized to depict a singlerepresentation of a betting table and/or betting objects.

In some embodiments, an image depicting depth can be constructed by animaging component 202 or image processing unit 204 from one or moreimages or frames captured from the same, similar, or differentperspectives. If two or more images are used, the image can beconstructed using image recognition matching similar or identicalobjects in the multiple images, time synchronization, or positionsynchronization.

FIG. 63 is a diagram of example sets of image frames 6310 (originalframes), 6320 (frames with obstruction), and 6330 (frames with noobstruction). Image processing engine 204 can process image data, forexample, from an RGB camera or an IR camera, and distinguish betweenobstructions and objects of interest, for example, a chip stack orbetting volume in the foreground. Image processing engine 204 can selectone or more frames where an object is not obstructed from image data orframes taken at near-contemporaneous time of that object. Exampleobstructions include partially coverage in the field of view the imagedata is collected at. A key frame image can be generated using theaverage of the selected one or more frames from a channel as well as thecorresponding frames from multiple other channels. The key frame canencode image data of a region of interest (e.g., including one or morechip stacks or bet volumes) with any obstructions of the region ofinterest removed.

FIG. 64 is a diagram of example image frames containing the same objecttaken at contemporaneous or near-contemporaneous time from the same orsimilar perspective. Each image frame is selected by image processingengine 204 as a key frame for each type of image data collected, forexample, colour channels (for key frame 6410), IR channels (for keyframe 6430), and data encoding representation of depth information (forkey frame 6420). Each key frame is selected for one or morecharacteristics, for example, absence of or minimal obstruction of abetting object, chip stack, or betting volume.

FIG. 65 is a diagram of example image frames containing the same objecttaken at contemporaneous or near-contemporaneous time from the same orsimilar perspective. Each image frame is selected by image processingengine 204 as a key frame for each type of image data collected, forexample, colour channels (for key frame 6510), IR channels (for keyframe 6530), and data encoding representation of depth information (forkey frame 6520). Each key frame is selected for one or morecharacteristics, for example, absence of or minimal obstruction of abetting object, chip stack, or betting volume.

FIGS. 98 and 67 are example representations of image data of a betvolume or stack of chips depicted as scale invariant data positioned ona two-dimensional grid. More specifically, images are depicted before(FIG. 98) and after (FIG. 67) scale invariance. For example, the imagedata can be modified to provide a defined aspect ratio, such that theimage can be depicted on accurate scale even if the image is resized.

FIGS. 97 and 66 is an example representation of image data of a betvolume or stack of chips depicted as rotation invariant data positionedon a two-dimensional grid. More specifically, images are depicted before(FIG. 97) and after (FIG. 66) rotation invariance.

FIG. 68 is an example of two sets of image data each containing arepresentation of image data collected over a red channel, greenchannel, blue channel, image(s) allowing generation of depthinformation, and IR channels. Each shows the key frame selected by theimage processing unit 204.

FIG. 69 is an example image showing normalization of image data of a betvolume captured by imaging component 202. In some embodiments, depthnormalization is not performed.

FIG. 70 is an example image set of image data, depicting a Y colourspace (image 7010), C_(b) colour space (image 7020), and C_(r) colourspace (image 7030) of the same bet volume (a stack of chips), where eachimage is generated from RGB image data captured by a imaging component202.

FIG. 71 are example representations of a betting object (a stack ofchips) 7110 and a two-dimension representation of its horizontalgradient 7120 depicting depth information. Image processing unit 204 isconfigured to generate the image depicting a horizontal gradient from aprocessed image or an image captured, for example, over RGB channels andIR channels. The horizontal gradient data is stored in a vector or arrayfor generation of a histogram. For each image, portion of image, regionof interest, or point of interest within same, a histogram can begenerated for each channel. All histograms generated for the same image,portion of image, region of interest, or point of interest within sameare concatenated or otherwise combined into a single histogram. Thesingle histogram is provided to a random forest classifier, which cangenerate a prediction of type, for example, chip type, colour, value, orimage type (e.g., background). This process can be repeated for eachpoint of interest in each horizontal row to determine a dominantclassification type for the row. This dominant classification canrepresent the chip type of a chip at the corresponding location. Thisprocess can be repeated for each row of points of interest to generate adominant classification type for each row, thereby determining the chiptype for a chip in a stack of chips.

FIG. 72 is an example representation of localization of two bettingobjects (here, chips) on a betting surface by image recognizer engine206. Image recognizer engine 206 can generate the display distinguishingthe two betting objects from other objects, including the bettingsurface and background objects, using different colours, for example,where the chips are pink and other objects are shades of green.Generation of a distinguishing display is not affected by the colour ofthe objects (e.g., felt colour), absence of chips in the bet area,background noise and chips, hands on the table at certain points of timeduring image capture, partial occlusion, or shadow effects.

FIG. 73 is an example image with an overlay depicting image data usedfor feature extraction and/or image recognition. Points of interestextractor 222 can select features from an image captured by imagingcomponent 202 or an image generated from same. The features can beselected to optimize training of or recognition by a classifier. Pointsof interest extractor 222 can select image data from a number of imagelocations along an x-direction as well as along a y-direction. Forexample, points of interest extractor 222 can manipulate the data tominimize weak matches from being extracted as a point of interest.Points of interest extractor 222 can shrink a ground truth area on bothsides of the defined regions horizontally. Every point of interest canbe stored as a unique representation in a data structure.

In some embodiments, points of interest extractor 222 selects points ofinterest by selecting points at an equal spacing apart in an x-directionwithin a region of interest. Points of interest extractor 222 selectsadditional points of interest at an equal spacing apart in ay-direction.

FIG. 74 is an example of the image in FIG. 73 with an overlay of theselected points of interest. As depicted, there are seven points ofinterest selected in each x-direction beginning at each of 29 points ofinterest selected in a y-direction. A different number of points can beselected at different intervals. Points of interest extractor 222 canselect the points of interest such that each point along a givenx-direction is selected at the same interval along the x-axis and thefirst point in each row is at the same x-axis value, such that thepoints of interest form straight columns and rows in a side view of abet object.

FIG. 75 is an example diagram depicting pixel representations of pointsof interest selected along a betting object, such as a chip. A centre ofthe chip is determined for each chip in the region of interest (e.g.,bet volume or stack of chips). For example, a centroid can be calculatedto determine the centre of the chip. The location of the chip centre canbe used in quantifying the number of bet objects (e.g., chips) for eachobject type (e.g., chip type). Histogram generator 224 generateshistogram data for each channel for each point of interest extracted.The histograms for each point of interest are concatenated and a singlehistogram for each point of interest is generated.

FIG. 76 is an example diagram depicting each histogram generated foreach point of interest. Each histogram depicts gradient (e.g.,horizontal) information of image data collected or generated from asingle channel. A channel can be a selection of wavelengths captured byimaging component 202 or additional channels generated from same, forexample, using a gradient analysis. The gradients can be represented bya histogram plotting numerical equivalents of colour intensity, forexample. The histograms for each channel for a single point of interestcan be concatenated.

FIG. 77 is an image of an example depiction of image data or regions ofinterest used for classification of each region of interest. A singleregion of interest depicted includes a single bet volume (e.g., onechip, a plurality of chips, or a bet volume). A dealer or administratorcan engage with system 1108, for example, at administrative interfacesubsystem 212, and label each bet volume for one or more betting objectsor components of image data. The labelled image data (and correspondinghistogram data) can be received by image recognizer engine 206, whichcan train one or more classifiers using the data. This can enable laterclassification of a bet volume, chip, or chip stack by chip type, forexample, denoting colour and/or value.

In some embodiments, a classifier can be trained using receivedlabelling, where a data indicating a point on a chip is received andused by processor 204 to define a point of interest and a windowincluding the point of interest and, for example, of a height and widthat least similar to the chip's size. The window can be used by processor204 (e.g., while training a classifier) for defining data with which togenerate a histogram for the point of interest. Processor 204 isconfigured in some embodiments to extract features at each point ofinterest, for example, using the corresponding histogram data. In someembodiments, processor 204 can train a random forest classifier usingreceived labels, for example, corresponding to chip type, on an imagesuch as a betting object in a region of interest.

FIG. 78 is a diagram of an example classification for each point ofinterest based on the histogram data for each point of interest. Imagerecognizer engine 206 can use a trained classifier to classify eachhistogram corresponding to each channel of a single point of interest.Image recognizer engine 206 can determine a dominant classification ofthe point of interest, for example, white, background, or none. Aclassification of “none” can result, for example, where the classifierdoes not reach a specified threshold of probability in thatclassification.

FIG. 79 is a diagram of an example process for training a classifier forpredicting bet volume type (e.g., chip type), for example, givenhistogram data (e.g., data encoding the horizontal gradient of imagedata) of a single point of interest. Random forest classifiers can beconstructed each from a boot strap sample sets of the original trainingset, where training set values are sampled with replacement. Each randomforest classifier can be constructed such that each node is split bychoosing a feature out of a random sample of x=˜√/(X) features. Thefeature at which a split is created can be selected as the feature thatgives the best information gain or optimized tree. Image recognizerengine 206 can vote on a set of predictions from each random forestresult, and a final prediction can be identified. Image recognizerengine 206 can provide the final prediction as a classification for thepoint of interest. The random forest can be constructed to optimize anyor several of the following parameters: Criterion (the function tomeasure the quality of a split), Features (the number of features toconsider when looking for the best split), Estimators (the number oftrees in the forest), Minimum sample split (the minimum number ofsamples required to split an internal node, Maximum depth of tree (themaximum depth of the tree), and Bootstrap (whether bootstrap samples areused when building trees).

In some embodiments, the trained random forest classifier is optimizedduring training in relation to at least one of (i) criterion for adecision split, (ii) a number of features for consideration fordetermining the criterion for the decision split, (iii) a number oftrees in the forest, (iv) a minimum number of samples required to splitan internal node, (v) a maximum depth of a tree, and (vi) use ofbootstrap samples.

In some embodiments, the trained random forest classifier can determinedominant classifications of each row in the grid of the points ofinterest (e.g., corresponding to a chip) or dominant classification fora point of interest. For example, the dominant classifications aredetermined by utilizing a trained random forest classifier where thetrained random forest classifier includes a plurality of classificationtrees and each representative histogram is classified by aclassification identified by a majority of the plurality of theclassification trees. In some embodiments, the dominant classificationsare determined by utilizing a trained random forest classifier and thetrained random forest classifier includes a plurality of classificationtrees and each representative histogram is classified by aclassification identified by a majority of the plurality of theclassification trees.

In some embodiments, the dominant classification for each row in thegrid of the points of interest is determined by a classification typerepresenting a largest proportion of the points of interest in the rowin the grid. For example, this is determined by majority voting in someembodiments.

FIG. 80 is a diagram of example decision trees used by random forestclassifiers for classifying a point of interest based on histogram dataplotting horizontal gradients of a single channel. A final predictioncan be generated based on a voting determination amongst the predictionsfrom each tree, where each tree is generated using a bootstrap sample ofthe training data. The training data can include points of interestclassifications and their corresponding histogram data.

FIG. 81 is a diagram of an example mapping between classification namesand colour representations. For example, image recognizer engine 206 canassociate colour name “0001-white” to classification data “0 0 1”generated by a classifier.

FIG. 82 is a diagram of an example depiction for a chip quantificationprocess by image recognizer engine 206. Points of interest are depictedin an x-direction. The median value can be selected as an indicator ofthe centre of a chip in a bet volume.

FIG. 83 is a diagram of an example depiction for a chip quantificationprocess by image recognizer engine 206. Image recognizer engine 206 candetermine the most frequently occurring value in each row of points ofinterest. This can be the dominant classification for the chipcorresponding to that row of points of interest. Image recognizer engine206 can determine the top of the stack of chips and use height, width,and image information to quantify the number of chips of each type in achip stack. This can be facilitated by determining the centroid andheight of each bet or chip volume, for example. A chip volume determinedas including a stack of chips having similar classifications.

In some embodiments, bet recognition system 200 or one or morecomponents thereof (e.g., image recognizer engine 206) can determine oneor more quantities of the one or more chip types of the one or morechips in one or more betting areas. This determination can be made by aprocess including identifying one or more chip volumes within a vectorrepresentation by grouping similar classifications in the vectorrepresentation (each chip volume representing a stack of chips havingsimilar classifications); determining a centroid for each chip volume ofthe one or more chip volumes; identifying a height for each chip volume;and estimating a number of chips in each chip volume by comparing thecentroid and the height of each chip volume with the physical geometriccharacteristics of the one or more chip types. The estimated number ofchips in each chip volume can be utilized to determine the one or morequantities of the one or more chip types.

In some embodiments, imaging components 202 can function in conjunctionwith or synchronization with illuminating components. Illuminatingcomponents are customizable to provide illumination in various colours,among others. As an example, FIG. 84 at 8400 depicts having severalilluminating components 8402, 8404 (e.g., LEDs) in use that are fullcolour and customizable for one or more tables, according to someembodiments. Each of the illuminating components 8402, 8404 isassociated with a corresponding spread 8412, 8414.

Customizable levels of brightness and color are useful where an operatoris seeking to configure the system in accordance with a desired theme orvisual effect. FIG. 85 at 8500 having several illuminating components8502, 8504 (e.g., LEDs) in use that have spreads of different color,respectively, according to some embodiments. FIG. 86 at 8600 depictsilluminating component 8602 with a custom color 8604, according to someembodiments. FIG. 87 is a photograph 8700 of illuminating component8702, depicting illuminating component 8702 from a perspective view,according to some embodiments.

FIG. 88 is an overhead photograph 8800 of physical hand count indicator8802, according to some embodiments. In this example, the physical handcount indicator 8802 includes an indicator light 8804 that is indicatesto a dealer that the system is running, and/or when a particular hand isopen or closed (8804 may be in a different color than 8802, forexample). Other indicators are possible, for example, there may beindicator elements that indicate when the system is running at a highlevel of accuracy or confidence, or others that indicate when a dealer'smovements or positioning is suboptimal and potentially impeding theproper operation of the system.

FIG. 89 is a rear perspective view 8900 of a bet recognition device 30that is mounted on a table surface, according to some embodiments. Asillustrated in this example, the bet recognition device 30 includes twodifferent bet recognition devices 8902 and 8904, which are positioned toobtain images of the gaming elements placed within their field of view.There may be more bet recognition devices, and the fields of view arenot always mutually exclusive. For example, increased accuracy may beobtained by providing bet recognition devices where the fields of viewoverlap.

FIG. 90 is a perspective view 9000 of a bet recognition device 30whereby the bet recognition device 30 include one or more interfaceareas 9002 and 9004 which are in electronic connection to either abackend game monitoring server 20 or a local game monitoringdevice/memory proximate bet recognition device 30. The interface areas9002 and 9004 may contain displays which are controlled for renderingvisual elements indicative of various information processed in relationto gaming, for example, in relation to betting activity. Where the oneor more interface areas 9002 and 9004 are in electronic connection witha connection to either a backend game monitoring server 20 or a local,directly obtained betting information can be displayed. In someembodiments, accuracy and tracking information can be provided so that adealer or other operator can, in real or near real-time, monitor howinformation associated with bet recognition device 30, such asoperational history, fraud/theft detection, bet/comp amounts, averagebet per hand, average bet per game, estimated accuracy level (e.g.,based on quality of illumination), among others.

The interface areas 9002 and 9004 in some embodiments are touchscreens.The touchscreens may be utilized, for example, by a dealer to copy andmove players across players position directly from the touchscreens. Thecopy and move functionality is useful as it facilitates convenienttracking of players as they move from seat to seat, and in someembodiments, the interface areas 9002 and 9004 allow for a single playerto play multiple betting positions each round of play. From acomplementary item providing and/or player profile tracking perspective,such a system may be particularly useful as it aids in obtaining moreaccurate play information. For example, a single player may wish to playin multiple spots at once in a game (e.g., playing three different seatsat a Blackjack table), or may wish to move from seat to seat (e.g.,because of a superstition that a particular seat is of better luck). ToApplicants' knowledge, such features are not available in conventionalplayer profile tracking systems and in both situations, opportunitiesmay be missed for player profile tracking.

FIG. 91 is an overhead photograph 9100 illustrating two controllers 9102and 9104, each being used to drive the bet recognition devices 30,according to some embodiments. The two controllers may have one or morememory modules, and may operate either individually or in combination.

Where the two controllers are operating in combination, the controllersmay be configured for redundant failover, accuracy comparisons (e.g.,where there are overlapping fields of view), bet data aggregation, amongothers. For example, if there are one or more controllers whose fieldsof view encompass all bet areas on a gaming surface, the one or morecontrollers, in communication with one another, may be configured toobtain and/or process data that can be utilized to model the entirety ofthe betting activity that is taking part on the table (assuming thatthat bet markers are visible relative to each of the optical systems),among others.

FIG. 92 is a photograph illustrating potential connections between theone or more processors according to some embodiments. In FIG. 92, at9200, two processors 9202 and 9204 are shown. Where there is a failure,the other processor may be able to provide failover support.

FIG. 93 is a photograph 9300 of a table without felt, illustrating areasupon which a physical retrofit can be applied to connect bet recognitiondevice 30. FIG. 94 is a photograph 9400 of under table connectors. Inthis photograph, a power port and an Ethernet connector port are shownalong with wires for connectivity. Various alternative computerconfigurations are possible (e.g., with an internal manageable networkswitch for one or more the networking addresses generated within anenclosure).

FIG. 95 is a rendering 9500 showing two interfaces that may be connectedto bet recognition devices 30, according to some embodiments. FIG. 96illustrates an example playing surface 9600 with betting areas 9602,9604, 9606, 9608, 9610, and 9612.

In some embodiments, system 1008 including any one or more of itscomponents, for example, processor 204, is implemented using one or morefield-programmable gate arrays (FPGAs). This can allow for customizedconfiguration and improved processing performance, including improvedprocessing speed or improved time for transmission of data. This canallow for faster (and more useful) image processing and classificationand quantification of bet objects, including chips or chip stacks.

An example embodiment of system 1008 will now be described. In theexample embodiment, system 1008 is installed at a betting surface.Multiple imaging components are positioned in relation to the bettingsurface, including with overlapping fields of view. System 1008 isconfigured to capture frames of RGB images, infrared images, and depthimages. Here, depth images can be generated by using time of flightsensors, structured light sensors, and/or triangulation based methodsusing stereo images. System 1008 is configured to extend the pluralityof channels by transforming captured image data from RGB to a differentcolour space where luminance is decoupled from chrominance, thetransformations yielding additional channels.

In the example embodiment, image processing engine 204 is configured toselect a key frame from the stream of images across the capturedchannels based on depth values. Here, the goal is to remove all theframes that have some obstruction between the cameras and the bet spotson the table. Once the clean frames are identified, the average of thecorresponding channels is taken to suppress any obstruction left. Thiswhole process is repeated for all the bet areas within their definedregion of interest. A region of interest is defined by image processingengine 204 as an area meeting threshold depth values. Image processingengine 204 is configured to generate different channels based on thederived key frame in RGB, depth and IR space. Image processing engine204 is configured to preprocess the key frame, including generating dataencoding rotation and scale invariant versions of the key frame beforegenerating other channels.

In some embodiments, image processing engine 204 is configured tocalibrate image data of the same gaming objects to generate depth data.In some embodiments, image processing engine 204 uses other image data,for example, IR data, in combination to generate depth data, decide howfar away an object is, or decide on an object's location. In someembodiments of the example embodiment, image processing engine 204determines a distance of an object based on how light or a light patternwarps over an object as captured by an IR imaging component. Thisdistance information is compared with other distance information of thesame object captured by a separate imaging component located a knowndistance away. In some embodiments of the example embodiment, two RGBcameras are used to generate depth information using triangulation.

In the example embodiment, image processing engine 204 is configured toextract and generate data to produce a horizontal gradient of at leastthe red, green, and blue channels.

Image processing engine 204 is then configured to perform chiplocalization by generating an image from the key frame with the portionsof the background of the image removed by removing data encodingportions of the key frame that do not meet one or more threshold depthvalues.

In the example embodiment, point of interest extractor 222 is configuredto select and extract data encoding points of interest within the keyframe. These points of interests are used to generate histogram-baseddescriptors that are used by the classifier to predict the type of abetting object, such as a chip type.

In the example embodiment, points of interest are selected by point ofinterest extractor 222 within a region of interest. Point of interestextractor 222 is configured to select points of interest using equalspacing in a horizontal direction within the region of interest.Additional points of interest can be selected using equal spacing in avertical direction within the region of interest. In some embodiments,the respective equal spacings can be different in horizontal andvertical directions.

In the example embodiment, histogram generator 224 is configured togenerate a histogram at each point of interest, with each histogram fora single point of interest corresponding to data from one or more of thechannels. Each histogram for a single point of interest is generatedbased on a small selection of image data encoding the portions of theimage immediately adjacent to the point of interest.

In the example embodiment, a 3×3 Sobel operator is applied to one ormore channels (e.g., the R, G, and B channels and/or other channels) ofimage data of the region of interest to generate a gradient. In someembodiments, other operators can be used to generate gradient data.

In the example embodiment, histogram generator 224 is configured togenerate a single descriptor of all channels against each point ofinterest by concatenating each histogram for each channel correspondingto a single point of interest. This concatenation or association of allhistogram data for each point of interest can provide a more robustdataset for classification of chip type by a trained classifier (or, inthe context of the generation of a trained classifier, can provide amore robust dataset for feature selection and training of a classifier).

This may help minimize noise in the representation or storage of theimage data or portions of the image data.

In the example embodiment, random forest classifier 226 is configured touse the concatenated histogram for a single point of interest forfeature extraction.

In the example embodiment, a gradient is calculated using a 3×3 Sobeloperator on one or more channels, for example, the R, G, and B channelsand/or channels in other colour spaces in order to generate horizontalgradients of the image data to capture the vertical texture in the betobjects, bet volume, or chips.

In the example embodiment, image recognizer engine 206 is configured toidentify a dominant classification for each row in a grid of the pointsof interest. The dominant classification is determined by majorityvoting or selection of the most frequently classified type amongst theclassified types for each point of interest in the row.

In the example embodiment, image recognizer engine 206 is configured todetermine one or more quantities of one or more chip types in a chipstack in the key frame image using the dominant classifications andcomparison to a physical geometric characteristic such as a centroiddetermined for each chip stack. Image recognizer engine 206 determinesthe centroid for a chip stack as the determined midpoint along an x-axisof a chip.

In the example embodiment, image recognizer engine 206 is configured tocause a data storage to maintain a data structure storing one or moredata fields representative of the determined one or more quantities ofone or more chip types of the one or more chips in the at least onebetting area.

The foregoing discussion provides many example embodiments of theinventive subject matter. Although each embodiment represents a singlecombination of inventive elements, the inventive subject matter isconsidered to include all possible combinations of the disclosedelements. Thus if one embodiment comprises elements A, B, and C, and asecond embodiment comprises elements B and D, then the inventive subjectmatter is also considered to include other remaining combinations of A,B, C, or D, even if not explicitly disclosed.

The embodiments of the devices, systems and methods described herein maybe implemented in a combination of both hardware and software. Theseembodiments may be implemented on programmable computers, each computerincluding at least one processor, a data storage system (includingvolatile memory or non-volatile memory or other data storage elements ora combination thereof), and at least one communication interface.

Program code is applied to input data to perform the functions describedherein and to generate output information. The output information isapplied to one or more output devices. In some embodiments, thecommunication interface may be a network communication interface. Inembodiments in which elements may be combined, the communicationinterface may be a software communication interface, such as those forinter-process communication. In still other embodiments, there may be acombination of communication interfaces implemented as hardware,software, and combination thereof.

Throughout the foregoing discussion, numerous references are maderegarding servers, services, interfaces, portals, platforms, or othersystems formed from computing devices.

It should be appreciated that the use of such terms is deemed torepresent one or more computing devices having at least one processorconfigured to execute software instructions stored on a computerreadable tangible, non-transitory medium. For example, a server caninclude one or more computers operating as a web server, databaseserver, or other type of computer server in a manner to fulfilldescribed roles, responsibilities, or functions.

The technical solution of embodiments may be in the form of a softwareproduct. The software product may be stored in a non-volatile ornon-transitory storage medium, which can be a compact disk read-onlymemory (CD-ROM), a USB flash disk, or a removable hard disk. Thesoftware product includes a number of instructions that enable acomputer device (personal computer, server, or network device) toexecute the methods provided by the embodiments.

The embodiments described herein are implemented by physical computerhardware, including computing devices, servers, receivers, transmitters,processors, memory, displays, and networks. The embodiments describedherein provide useful physical machines and particularly configuredcomputer hardware arrangements.

Although the embodiments have been described in detail, it should beunderstood that various changes, substitutions and alterations can bemade herein.

Moreover, the scope of the present application is not intended to belimited to the particular embodiments of the process, machine,manufacture, composition of matter, means, methods and steps describedin the specification.

As can be understood, the examples described above and illustrated areintended to be exemplary only.

What is claimed is:
 1. A device for monitoring game activities at agaming table comprising: an imaging component positioned on the gamingtable or proximate thereto to capture image data corresponding to one ormore chips positioned in at least one betting area on a gaming surfaceof the respective gaming table, the imaging component positioned tocapture images of the gaming surface of the respective gaming table; aprocessor configured to pre-process the captured image data to filter atleast a portion of background image data to generate a compressed set ofimage data of the one or more chips free of the background image data;the processor further configured to process the compressed set of imagedata to establish a two-dimensional grid comprising points of interestoverlaid over the compressed set of image data, and for each point ofinterest, classify the point of interest based on an analysis of acorresponding representative histogram generated based on the image datacorresponding to the corresponding point of interest; the processorfurther configured to identify a dominant classification for each row inthe grid of the points of interest, the dominant classification recordedin a data structure to establish a vector representation of the one ormore chips in the at least one betting area; the processor furtherconfigured to determine one or more quantities of one or more chip typesof the one or more chips in the at least one betting area by processingthe vector representation based at least on a comparison with physicalgeometric characteristics of the one or more chip types; a data storageconfigured to maintain an output data structure storing one or more datafields representative of the determined one or more quantities of theone or more chip types of the one or more chips in the at least onebetting area; and wherein the captured image data is captured across aplurality of channels including at least a red channel, a green channel,a blue channel, a depth information channel, and an infrared channel;and wherein each representative histogram is an aggregated histogramgenerated from combining histograms generated for each channel of theplurality of the channels.
 2. The device of claim 1, further comprisinga communication link configured for transmitting the output datastructure or a subset of the output data structure to generate bettingdata for the gaming table, the betting data including betting amountsfor the at least one betting area.
 3. The device of claim 1, wherein theprocessor is configured to extend the plurality of channels bytransforming the captured image data from RGB to a different colourspace where luminance is decoupled from chrominance, the transformationyielding additional channels in the plurality of channels.
 4. The deviceof claim 1, wherein horizontal gradients are calculated using a 3×3Sobel operator in order to capture one or more vertical textures in theone or more chips.
 5. The device of claim 1, wherein the captured imagedata is represented by an aggregated frame corresponding to averageimage data of image data captured across a duration of time to reducetransient effects arising from temporary visual obstructions of theimaging component.
 6. The device of claim 1, wherein the processor isfurther configured to pre-process the captured image data to apply atleast one of rotation and scale invariance.
 7. The device of claim 1,wherein the dominant classifications are determined by utilizing atrained random forest classifier; and wherein the trained random forestclassifier is optimized during training in relation to at least one of(i) criterion for a decision split, (ii) a number of features forconsideration for determining the criterion for the decision split,(iii) a number of trees in the forest, (iv) a minimum number of samplesrequired to split an internal node, (v) a maximum depth of a tree, and(vi) use of bootstrap samples.
 8. The device of claim 1, wherein thedominant classification for each row in the grid of the points ofinterest is determined by a classification type representing a largestproportion of the points of interest in the row in the grid.
 9. Thedevice of claim 1, wherein the determination of the one or morequantities of the one or more chip types of the one or more chips in theat least one betting area includes: identifying one or more chip volumeswithin the vector representation by grouping similar classifications inthe vector representation, each chip volume representing a stack ofchips having similar classifications; determining a centroid for eachchip volume of the one or more chip volumes; identifying a height foreach chip volume; and estimating a number of chips in each chip volumeby comparing the centroid and the height of each chip volume with thephysical geometric characteristics of the one or more chip types, theestimated number of chips in each chip volume utilized to determine theone or more quantities of the one or more chip types.
 10. A method formonitoring game activities at a gaming table comprising: capturing imagedata corresponding to one or more chips positioned in at least onebetting area on a gaming surface of the respective gaming table;pre-processing the captured image data to filter at least a portion ofbackground image data to generate a compressed set of image data of theone or more chips free of the background image data; processing thecompressed set of image data to establish a two-dimensional gridcomprising points of interest overlaid on the compressed set of imagedata, and for each point of interest, classifying the point of interestbased on an analysis of a corresponding representative histogramgenerated based on the image data corresponding to the correspondingpoint of interest; identifying a dominant classification for each row inthe grid of the points of interest, the dominant classification recordedin a data structure to establish a vector representation of the one ormore chips in the at least one betting area; determining one or morequantities of one or more chip types of the one or more chips in the atleast one betting area by processing the vector representation based onat least a comparison with physical geometric characteristics of the oneor more chip types; maintaining an output data structure storing one ormore data fields representative of the determined one or more quantitiesof the one or more chip types of the one or more chips in the at leastone betting area; and wherein the captured image data is captured acrossa plurality of channels including at least a red channel, a greenchannel, a blue channel, a depth information channel, and an infraredchannel; and wherein each representative histogram is an aggregatedhistogram generated from combining histograms generated for each channelof the plurality of the channels.
 11. The method of claim 10, furthercomprising transmitting the output data structure or a subset of theoutput data structure to generate betting data for the gaming table, thebetting data including betting amounts for the at least one bettingarea.
 12. The method of claim 10, further comprising extending theplurality of channels by transforming the captured image data from RGBto a different colour space where luminance is decoupled fromchrominance, the transformation yielding additional channels in theplurality of channels.
 13. The method of claim 10, wherein horizontalgradients are calculated using a 3×3 Sobel operator in order to captureone or more vertical textures in the one or more chips.
 14. The methodof claim 10, wherein the captured image data is represented by anaggregated frame corresponding to average image data of image datacaptured across a duration of time to reduce transient effects arisingfrom temporary visual obstructions of an imaging component.
 15. Themethod of claim 10, wherein the dominant classifications are determinedby utilizing a trained random forest classifier; and wherein the trainedrandom forest classifier is optimized during training in relation to atleast one of (i) criterion for a decision split, (ii) a number offeatures for consideration for determining the criterion for thedecision split, (iii) a number of trees in the forest, (iv) a minimumnumber of samples required to split an internal node, (v) a maximumdepth of a tree, and (vi) use of bootstrap samples.
 16. The method ofclaim 10, wherein the dominant classification for each row in the gridof the points of interest is determined by a classification typerepresenting a largest proportion of the points of interest in the rowin the grid.
 17. The method of claim 10, wherein the determination ofthe one or more quantities of the one or more chip types of the one ormore chips in the at least one betting area includes: identifying one ormore chip volumes within the vector representation by grouping similarclassifications in the vector representation, each chip volumerepresenting a stack of chips having similar classifications;determining a centroid for each chip volume of the one or more chipvolumes; identifying a height for each chip volume; and estimating anumber of chips in each chip volume by comparing the centroid and theheight of each chip volume with the physical geometric characteristicsof the one or more chip types, the estimated number of chips in eachchip volume utilized to determine the one or more quantities of the oneor more chip types.
 18. A non-transitory computer-readable storagemedium comprising computer-executable instructions which, when executed,cause a processor to perform a method for monitoring game activities ata gaming table, the method comprising: capturing image datacorresponding to one or more chips positioned in at least one bettingarea on a gaming surface of the gaming table; pre-processing thecaptured image data to filter at least a portion of background imagedata to generate a compressed set of image data of the one or more chipsfree of the background image data; processing the compressed set ofimage data to establish a two-dimensional grid comprising points ofinterest overlaid over the compressed set of image data, and for eachpoint of interest, classifying the point of interest based on ananalysis of a corresponding representative histogram generated based onthe image data corresponding to the corresponding point of interest;identifying a dominant classification for each row in the grid of thepoints of interest, the dominant classification recorded in a datastructure to establish a vector representation of the one or more chipsin the at least one betting area; determining one or more quantities ofone or more chip types of the one or more chips in the at least onebetting area by processing the vector representation based on at least acomparison with physical geometric characteristics of the one or morechip types; maintaining an output data structure storing one or moredata fields representative of the determined one or more quantities ofthe one or more chip types of the one or more chips in the at least onebetting area; and wherein the captured image data is captured across aplurality of channels including at least a red channel, a green channel,a blue channel, a depth information channel, and an infrared channel;and wherein each representative histogram is an aggregated histogramgenerated from combining histograms generated for each channel of theplurality of the channels.